From 7084aac726c512c7818b18b8df79189a4e474077 Mon Sep 17 00:00:00 2001 From: Noy Arie Date: Mon, 22 May 2023 15:39:17 +0300 Subject: [PATCH 01/16] add get_invocations_by_ids (macro, fetcher, and api) --- .../monitor/api/invocations/invocations.py | 7 +++++++ .../macros/get_invocations_by_ids.sql | 19 +++++++++++++++++++ .../fetchers/invocations/invocations.py | 14 ++++++++++++++ 3 files changed, 40 insertions(+) create mode 100644 elementary/monitor/dbt_project/macros/get_invocations_by_ids.sql diff --git a/elementary/monitor/api/invocations/invocations.py b/elementary/monitor/api/invocations/invocations.py index 4756eb816..2adddf3a0 100644 --- a/elementary/monitor/api/invocations/invocations.py +++ b/elementary/monitor/api/invocations/invocations.py @@ -37,3 +37,10 @@ def get_invocation_by_id( ) else: raise NotImplementedError + + def get_invocations_by_ids( + self, invocations_ids: list[str] + ) -> DbtInvocationSchema: + return self.invocations_fetcher.get_invocations_by_ids( + macro_args=dict(ids=invocations_ids) + ) diff --git a/elementary/monitor/dbt_project/macros/get_invocations_by_ids.sql b/elementary/monitor/dbt_project/macros/get_invocations_by_ids.sql new file mode 100644 index 000000000..9b78b3ea7 --- /dev/null +++ b/elementary/monitor/dbt_project/macros/get_invocations_by_ids.sql @@ -0,0 +1,19 @@ +{% macro get_invocations_by_ids(ids) %} + {% set database, schema = elementary.target_database(), target.schema %} + {% set invocations_relation = adapter.get_relation(database, schema, 'dbt_invocations') %} + {% if not invocations_relation %} + {% do elementary.edr_log('failed getting invocations relation') %} + {% do return(none) %} + {% endif %} + + {% set get_invocations_query %} + select * from {{ invocations_relation }} where invocation_id in {{ elementary.strings_list_to_tuple(ids) }} + {% endset %} + {% set result = elementary.run_query(get_invocations_query) %} + {% if not result %} + {% do elementary.edr_log('no invocations were found') %} + {% do return(none) %} + {% endif %} + + {% do return(elementary.agate_to_dicts(result)) %} +{% endmacro %} diff --git a/elementary/monitor/fetchers/invocations/invocations.py b/elementary/monitor/fetchers/invocations/invocations.py index 927f22f9f..39d49979c 100644 --- a/elementary/monitor/fetchers/invocations/invocations.py +++ b/elementary/monitor/fetchers/invocations/invocations.py @@ -21,3 +21,17 @@ def get_test_last_invocation( else: logger.warning(f"Could not find invocation by filter: {macro_args}") return DbtInvocationSchema() + + def get_invocations_by_ids( + self, macro_args: Optional[dict] = None + ) -> [DbtInvocationSchema]: + invocations_response = self.dbt_runner.run_operation( + macro_name="get_invocations_by_ids", macro_args=macro_args + ) + invocation_results = ( + json.loads(invocations_response[0]) if invocations_response else [] + ) + invocation_results = [ + DbtInvocationSchema(**invocation_result) for invocation_result in invocation_results + ] + return invocation_results From bfe4a7a8a03d4e8e0a545ada454b99ae2fcf6378 Mon Sep 17 00:00:00 2001 From: Noy Arie Date: Mon, 22 May 2023 15:40:23 +0300 Subject: [PATCH 02/16] add get_resources_latest_invocation (macro, fetcher, and api) --- .../monitor/api/invocations/invocations.py | 5 +++++ .../get_resources_latest_invocation.sql | 22 +++++++++++++++++++ .../fetchers/invocations/invocations.py | 16 +++++++++++++- 3 files changed, 42 insertions(+), 1 deletion(-) create mode 100644 elementary/monitor/dbt_project/macros/get_resources_latest_invocation.sql diff --git a/elementary/monitor/api/invocations/invocations.py b/elementary/monitor/api/invocations/invocations.py index 2adddf3a0..96af9468b 100644 --- a/elementary/monitor/api/invocations/invocations.py +++ b/elementary/monitor/api/invocations/invocations.py @@ -1,3 +1,5 @@ +from typing import Dict + from elementary.clients.api.api_client import APIClient from elementary.clients.dbt.base_dbt_runner import BaseDbtRunner from elementary.monitor.fetchers.invocations.invocations import InvocationsFetcher @@ -44,3 +46,6 @@ def get_invocations_by_ids( return self.invocations_fetcher.get_invocations_by_ids( macro_args=dict(ids=invocations_ids) ) + + def get_resources_latest_invocation(self) -> Dict[str, str]: + return self.invocations_fetcher.get_resources_latest_invocation() diff --git a/elementary/monitor/dbt_project/macros/get_resources_latest_invocation.sql b/elementary/monitor/dbt_project/macros/get_resources_latest_invocation.sql new file mode 100644 index 000000000..cdc6c0025 --- /dev/null +++ b/elementary/monitor/dbt_project/macros/get_resources_latest_invocation.sql @@ -0,0 +1,22 @@ +{% macro get_resources_latest_invocation() %} + {% set dbt_run_results = ref('dbt_run_results') %} + {%- if elementary.relation_exists(dbt_run_results) -%} + {% set get_resources_latest_invocation_query %} + with row_numbered_run_results as ( + select + *, + ROW_NUMBER() OVER (PARTITION BY unique_id ORDER BY generated_at DESC) AS row_number + from {{ dbt_run_results }} + ), + latest_run_results as ( + select * + from row_numbered_run_results + where row_number = 1 + ) + + select unique_id, invocation_id from latest_run_results + {% endset %} + {% set run_invocations_agate = run_query(get_resources_latest_invocation_query) %} + {% do return(elementary.agate_to_dicts(run_invocations_agate)) %} + {%- endif -%} +{% endmacro %} diff --git a/elementary/monitor/fetchers/invocations/invocations.py b/elementary/monitor/fetchers/invocations/invocations.py index 39d49979c..fd14ac690 100644 --- a/elementary/monitor/fetchers/invocations/invocations.py +++ b/elementary/monitor/fetchers/invocations/invocations.py @@ -1,5 +1,5 @@ import json -from typing import Optional +from typing import Optional, Dict from elementary.clients.fetcher.fetcher import FetcherClient from elementary.monitor.fetchers.invocations.schema import DbtInvocationSchema @@ -35,3 +35,17 @@ def get_invocations_by_ids( DbtInvocationSchema(**invocation_result) for invocation_result in invocation_results ] return invocation_results + + def get_resources_latest_invocation(self) -> Dict[str, str]: + response = self.dbt_runner.run_operation( + macro_name="get_resources_latest_invocation" + ) + resources_latest_invocation_results = ( + json.loads(response[0]) if response else [] + ) + + resources_latest_invocation_dict = dict() + for result in resources_latest_invocation_results: + resources_latest_invocation_dict[result['unique_id']] = result['invocation_id'] + + return resources_latest_invocation_dict From 18fe5606b94fbd4684c47db8f3ab12d39a485505 Mon Sep 17 00:00:00 2001 From: Noy Arie Date: Mon, 22 May 2023 15:40:53 +0300 Subject: [PATCH 03/16] add invocation schema fields --- elementary/monitor/fetchers/invocations/schema.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/elementary/monitor/fetchers/invocations/schema.py b/elementary/monitor/fetchers/invocations/schema.py index 60dab98d9..7b8d3c93b 100644 --- a/elementary/monitor/fetchers/invocations/schema.py +++ b/elementary/monitor/fetchers/invocations/schema.py @@ -12,6 +12,10 @@ class DbtInvocationSchema(BaseModel): command: Optional[str] = None selected: Optional[str] = None full_refresh: Optional[bool] = None + job_url: Optional[str] = None + job_name: Optional[str] = None + job_id: Optional[str] = None + orchestrator: Optional[str] = None @validator("detected_at", pre=True) def format_detected_at(cls, detected_at): From e6410467633382a2f66c3d052f1394853fea590a Mon Sep 17 00:00:00 2001 From: Noy Arie Date: Mon, 22 May 2023 15:46:14 +0300 Subject: [PATCH 04/16] add data to report --- elementary/monitor/api/report/report.py | 9 +++++++++ elementary/monitor/api/report/schema.py | 2 ++ 2 files changed, 11 insertions(+) diff --git a/elementary/monitor/api/report/report.py b/elementary/monitor/api/report/report.py index 1508860e3..f52bd6a4b 100644 --- a/elementary/monitor/api/report/report.py +++ b/elementary/monitor/api/report/report.py @@ -3,6 +3,7 @@ from elementary.clients.api.api_client import APIClient from elementary.monitor.api.filters.filters import FiltersAPI +from elementary.monitor.api.invocations.invocations import InvocationsAPI from elementary.monitor.api.lineage.lineage import LineageAPI from elementary.monitor.api.models.models import ModelsAPI from elementary.monitor.api.models.schema import ( @@ -44,6 +45,7 @@ def get_report_data( sidebar_api = SidebarAPI(dbt_runner=self.dbt_runner) lineage_api = LineageAPI(dbt_runner=self.dbt_runner) filters_api = FiltersAPI(dbt_runner=self.dbt_runner) + invocations_api = InvocationsAPI(dbt_runner=self.dbt_runner) models = models_api.get_models(exclude_elementary_models) sources = models_api.get_sources() @@ -87,6 +89,11 @@ def get_report_data( serializable_filters = filters.dict() serializable_lineage = lineage.dict() + resources_latest_invocation = invocations_api.get_resources_latest_invocation() + invocations = invocations_api.get_invocations_by_ids( + invocations_ids=list(set(resources_latest_invocation.values())) + ) + report_data = ReportDataSchema( creation_time=get_now_utc_iso_format(), days_back=days_back, @@ -102,6 +109,8 @@ def get_report_data( model_runs_totals=serializable_model_runs_totals, filters=serializable_filters, lineage=serializable_lineage, + invocations=invocations, + resources_latest_invocation=resources_latest_invocation, env=dict(project_name=project_name, env=env), ) return report_data, None diff --git a/elementary/monitor/api/report/schema.py b/elementary/monitor/api/report/schema.py index 7d9d281ae..f1fc0b024 100644 --- a/elementary/monitor/api/report/schema.py +++ b/elementary/monitor/api/report/schema.py @@ -18,5 +18,7 @@ class ReportDataSchema(BaseModel): model_runs_totals: dict = dict() filters: dict = dict() lineage: dict = dict() + invocations: list = list() + resources_latest_invocation: dict = dict() env: dict = dict() tracking: Optional[dict] = None From 1edd25930dcab906da55aa749f87971cee17f2d4 Mon Sep 17 00:00:00 2001 From: Noy Arie Date: Tue, 23 May 2023 12:34:51 +0300 Subject: [PATCH 05/16] run black --- elementary/monitor/api/invocations/invocations.py | 4 +--- elementary/monitor/api/report/report.py | 4 +++- elementary/monitor/fetchers/invocations/invocations.py | 7 +++++-- 3 files changed, 9 insertions(+), 6 deletions(-) diff --git a/elementary/monitor/api/invocations/invocations.py b/elementary/monitor/api/invocations/invocations.py index 96af9468b..4dd2b742f 100644 --- a/elementary/monitor/api/invocations/invocations.py +++ b/elementary/monitor/api/invocations/invocations.py @@ -40,9 +40,7 @@ def get_invocation_by_id( else: raise NotImplementedError - def get_invocations_by_ids( - self, invocations_ids: list[str] - ) -> DbtInvocationSchema: + def get_invocations_by_ids(self, invocations_ids: list[str]) -> DbtInvocationSchema: return self.invocations_fetcher.get_invocations_by_ids( macro_args=dict(ids=invocations_ids) ) diff --git a/elementary/monitor/api/report/report.py b/elementary/monitor/api/report/report.py index f52bd6a4b..4b77c3fe0 100644 --- a/elementary/monitor/api/report/report.py +++ b/elementary/monitor/api/report/report.py @@ -89,7 +89,9 @@ def get_report_data( serializable_filters = filters.dict() serializable_lineage = lineage.dict() - resources_latest_invocation = invocations_api.get_resources_latest_invocation() + resources_latest_invocation = ( + invocations_api.get_resources_latest_invocation() + ) invocations = invocations_api.get_invocations_by_ids( invocations_ids=list(set(resources_latest_invocation.values())) ) diff --git a/elementary/monitor/fetchers/invocations/invocations.py b/elementary/monitor/fetchers/invocations/invocations.py index fd14ac690..cd0b0b13b 100644 --- a/elementary/monitor/fetchers/invocations/invocations.py +++ b/elementary/monitor/fetchers/invocations/invocations.py @@ -32,7 +32,8 @@ def get_invocations_by_ids( json.loads(invocations_response[0]) if invocations_response else [] ) invocation_results = [ - DbtInvocationSchema(**invocation_result) for invocation_result in invocation_results + DbtInvocationSchema(**invocation_result) + for invocation_result in invocation_results ] return invocation_results @@ -46,6 +47,8 @@ def get_resources_latest_invocation(self) -> Dict[str, str]: resources_latest_invocation_dict = dict() for result in resources_latest_invocation_results: - resources_latest_invocation_dict[result['unique_id']] = result['invocation_id'] + resources_latest_invocation_dict[result["unique_id"]] = result[ + "invocation_id" + ] return resources_latest_invocation_dict From 8a465463c1384e535d079f95c213f5f6d4502e1e Mon Sep 17 00:00:00 2001 From: Noy Arie Date: Tue, 23 May 2023 12:45:46 +0300 Subject: [PATCH 06/16] isort --- elementary/monitor/fetchers/invocations/invocations.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/elementary/monitor/fetchers/invocations/invocations.py b/elementary/monitor/fetchers/invocations/invocations.py index cd0b0b13b..54743e8b0 100644 --- a/elementary/monitor/fetchers/invocations/invocations.py +++ b/elementary/monitor/fetchers/invocations/invocations.py @@ -1,5 +1,5 @@ import json -from typing import Optional, Dict +from typing import Dict, Optional from elementary.clients.fetcher.fetcher import FetcherClient from elementary.monitor.fetchers.invocations.schema import DbtInvocationSchema From 933b9c37b959f323839264333fc71624c4728566 Mon Sep 17 00:00:00 2001 From: Noy Arie Date: Sun, 28 May 2023 10:15:15 +0300 Subject: [PATCH 07/16] format import --- elementary/monitor/api/report/report.py | 14 ++++++-------- 1 file changed, 6 insertions(+), 8 deletions(-) diff --git a/elementary/monitor/api/report/report.py b/elementary/monitor/api/report/report.py index 4b77c3fe0..91a10a8e1 100644 --- a/elementary/monitor/api/report/report.py +++ b/elementary/monitor/api/report/report.py @@ -6,14 +6,12 @@ from elementary.monitor.api.invocations.invocations import InvocationsAPI from elementary.monitor.api.lineage.lineage import LineageAPI from elementary.monitor.api.models.models import ModelsAPI -from elementary.monitor.api.models.schema import ( - ModelCoverageSchema, - ModelRunsSchema, - NormalizedExposureSchema, - NormalizedModelSchema, - NormalizedSourceSchema, - TotalsSchema, -) +from elementary.monitor.api.models.schema import (ModelCoverageSchema, + ModelRunsSchema, + NormalizedExposureSchema, + NormalizedModelSchema, + NormalizedSourceSchema, + TotalsSchema) from elementary.monitor.api.report.schema import ReportDataSchema from elementary.monitor.api.sidebar.sidebar import SidebarAPI from elementary.monitor.api.tests.schema import TestResultSchema, TestRunSchema From 9eb3bacb0092ecd749ee8c39c40d0070efc02d72 Mon Sep 17 00:00:00 2001 From: Noy Arie Date: Sun, 28 May 2023 10:58:06 +0300 Subject: [PATCH 08/16] format + style --- elementary/monitor/api/invocations/invocations.py | 6 ++++-- elementary/monitor/api/report/report.py | 14 ++++++++------ .../monitor/fetchers/invocations/invocations.py | 10 +++------- 3 files changed, 15 insertions(+), 15 deletions(-) diff --git a/elementary/monitor/api/invocations/invocations.py b/elementary/monitor/api/invocations/invocations.py index 4dd2b742f..e6ca6e08d 100644 --- a/elementary/monitor/api/invocations/invocations.py +++ b/elementary/monitor/api/invocations/invocations.py @@ -1,4 +1,4 @@ -from typing import Dict +from typing import Dict, List from elementary.clients.api.api_client import APIClient from elementary.clients.dbt.base_dbt_runner import BaseDbtRunner @@ -40,7 +40,9 @@ def get_invocation_by_id( else: raise NotImplementedError - def get_invocations_by_ids(self, invocations_ids: list[str]) -> DbtInvocationSchema: + def get_invocations_by_ids( + self, invocations_ids: list[str] + ) -> List[DbtInvocationSchema]: return self.invocations_fetcher.get_invocations_by_ids( macro_args=dict(ids=invocations_ids) ) diff --git a/elementary/monitor/api/report/report.py b/elementary/monitor/api/report/report.py index 91a10a8e1..4b77c3fe0 100644 --- a/elementary/monitor/api/report/report.py +++ b/elementary/monitor/api/report/report.py @@ -6,12 +6,14 @@ from elementary.monitor.api.invocations.invocations import InvocationsAPI from elementary.monitor.api.lineage.lineage import LineageAPI from elementary.monitor.api.models.models import ModelsAPI -from elementary.monitor.api.models.schema import (ModelCoverageSchema, - ModelRunsSchema, - NormalizedExposureSchema, - NormalizedModelSchema, - NormalizedSourceSchema, - TotalsSchema) +from elementary.monitor.api.models.schema import ( + ModelCoverageSchema, + ModelRunsSchema, + NormalizedExposureSchema, + NormalizedModelSchema, + NormalizedSourceSchema, + TotalsSchema, +) from elementary.monitor.api.report.schema import ReportDataSchema from elementary.monitor.api.sidebar.sidebar import SidebarAPI from elementary.monitor.api.tests.schema import TestResultSchema, TestRunSchema diff --git a/elementary/monitor/fetchers/invocations/invocations.py b/elementary/monitor/fetchers/invocations/invocations.py index 54743e8b0..58d1e1421 100644 --- a/elementary/monitor/fetchers/invocations/invocations.py +++ b/elementary/monitor/fetchers/invocations/invocations.py @@ -45,10 +45,6 @@ def get_resources_latest_invocation(self) -> Dict[str, str]: json.loads(response[0]) if response else [] ) - resources_latest_invocation_dict = dict() - for result in resources_latest_invocation_results: - resources_latest_invocation_dict[result["unique_id"]] = result[ - "invocation_id" - ] - - return resources_latest_invocation_dict + resources_latest_invocation_map = {result["unique_id"]: result["invocation_id"] for result in + resources_latest_invocation_results} + return resources_latest_invocation_map From 1801232b68f1248371a9f7577fe02d77328a7975 Mon Sep 17 00:00:00 2001 From: Noy Arie Date: Sun, 28 May 2023 11:19:10 +0300 Subject: [PATCH 09/16] refactor --- .../monitor/api/invocations/invocations.py | 4 +--- .../monitor/fetchers/invocations/invocations.py | 17 +++++++++++------ 2 files changed, 12 insertions(+), 9 deletions(-) diff --git a/elementary/monitor/api/invocations/invocations.py b/elementary/monitor/api/invocations/invocations.py index e6ca6e08d..d781d3b1d 100644 --- a/elementary/monitor/api/invocations/invocations.py +++ b/elementary/monitor/api/invocations/invocations.py @@ -43,9 +43,7 @@ def get_invocation_by_id( def get_invocations_by_ids( self, invocations_ids: list[str] ) -> List[DbtInvocationSchema]: - return self.invocations_fetcher.get_invocations_by_ids( - macro_args=dict(ids=invocations_ids) - ) + return self.invocations_fetcher.get_invocations_by_ids(invocations_ids) def get_resources_latest_invocation(self) -> Dict[str, str]: return self.invocations_fetcher.get_resources_latest_invocation() diff --git a/elementary/monitor/fetchers/invocations/invocations.py b/elementary/monitor/fetchers/invocations/invocations.py index 58d1e1421..ef95fa272 100644 --- a/elementary/monitor/fetchers/invocations/invocations.py +++ b/elementary/monitor/fetchers/invocations/invocations.py @@ -1,5 +1,5 @@ import json -from typing import Dict, Optional +from typing import Dict, List, Optional from elementary.clients.fetcher.fetcher import FetcherClient from elementary.monitor.fetchers.invocations.schema import DbtInvocationSchema @@ -23,10 +23,13 @@ def get_test_last_invocation( return DbtInvocationSchema() def get_invocations_by_ids( - self, macro_args: Optional[dict] = None - ) -> [DbtInvocationSchema]: + self, invocations_ids: list[str] + ) -> List[DbtInvocationSchema]: invocations_response = self.dbt_runner.run_operation( - macro_name="get_invocations_by_ids", macro_args=macro_args + macro_name="get_invocations_by_ids", + macro_args={ + "ids": invocations_ids, + }, ) invocation_results = ( json.loads(invocations_response[0]) if invocations_response else [] @@ -45,6 +48,8 @@ def get_resources_latest_invocation(self) -> Dict[str, str]: json.loads(response[0]) if response else [] ) - resources_latest_invocation_map = {result["unique_id"]: result["invocation_id"] for result in - resources_latest_invocation_results} + resources_latest_invocation_map = { + result["unique_id"]: result["invocation_id"] + for result in resources_latest_invocation_results + } return resources_latest_invocation_map From 34cd3b5aeb87c676e65d8e2b7170a902732626c7 Mon Sep 17 00:00:00 2001 From: Noy Arie Date: Sun, 28 May 2023 11:22:15 +0300 Subject: [PATCH 10/16] remove relation_exists for dbt run results --- .../get_resources_latest_invocation.sql | 34 +++++++++---------- 1 file changed, 16 insertions(+), 18 deletions(-) diff --git a/elementary/monitor/dbt_project/macros/get_resources_latest_invocation.sql b/elementary/monitor/dbt_project/macros/get_resources_latest_invocation.sql index cdc6c0025..7e5391564 100644 --- a/elementary/monitor/dbt_project/macros/get_resources_latest_invocation.sql +++ b/elementary/monitor/dbt_project/macros/get_resources_latest_invocation.sql @@ -1,22 +1,20 @@ {% macro get_resources_latest_invocation() %} {% set dbt_run_results = ref('dbt_run_results') %} - {%- if elementary.relation_exists(dbt_run_results) -%} - {% set get_resources_latest_invocation_query %} - with row_numbered_run_results as ( - select - *, - ROW_NUMBER() OVER (PARTITION BY unique_id ORDER BY generated_at DESC) AS row_number - from {{ dbt_run_results }} - ), - latest_run_results as ( - select * - from row_numbered_run_results - where row_number = 1 - ) + {% set get_resources_latest_invocation_query %} + with row_numbered_run_results as ( + select + *, + ROW_NUMBER() OVER (PARTITION BY unique_id ORDER BY generated_at DESC) AS row_number + from {{ dbt_run_results }} + ), + latest_run_results as ( + select * + from row_numbered_run_results + where row_number = 1 + ) - select unique_id, invocation_id from latest_run_results - {% endset %} - {% set run_invocations_agate = run_query(get_resources_latest_invocation_query) %} - {% do return(elementary.agate_to_dicts(run_invocations_agate)) %} - {%- endif -%} + select unique_id, invocation_id from latest_run_results + {% endset %} + {% set run_invocations_agate = run_query(get_resources_latest_invocation_query) %} + {% do return(elementary.agate_to_dicts(run_invocations_agate)) %} {% endmacro %} From d561bcf049befb9d93651295b644b0c5858e61f9 Mon Sep 17 00:00:00 2001 From: Noy Arie Date: Sun, 28 May 2023 11:24:05 +0300 Subject: [PATCH 11/16] rename --- .../dbt_project/macros/get_resources_latest_invocation.sql | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/elementary/monitor/dbt_project/macros/get_resources_latest_invocation.sql b/elementary/monitor/dbt_project/macros/get_resources_latest_invocation.sql index 7e5391564..75ee2e60f 100644 --- a/elementary/monitor/dbt_project/macros/get_resources_latest_invocation.sql +++ b/elementary/monitor/dbt_project/macros/get_resources_latest_invocation.sql @@ -1,7 +1,7 @@ {% macro get_resources_latest_invocation() %} {% set dbt_run_results = ref('dbt_run_results') %} {% set get_resources_latest_invocation_query %} - with row_numbered_run_results as ( + with ordered_run_results as ( select *, ROW_NUMBER() OVER (PARTITION BY unique_id ORDER BY generated_at DESC) AS row_number @@ -9,7 +9,7 @@ ), latest_run_results as ( select * - from row_numbered_run_results + from ordered_run_results where row_number = 1 ) From e5dd99f7d6ac10e6d7c46a7a105ae27ab490f085 Mon Sep 17 00:00:00 2001 From: Noy Arie Date: Sun, 28 May 2023 11:54:10 +0300 Subject: [PATCH 12/16] refactor + remove edr_log --- .../macros/get_invocations_by_ids.sql | 30 ++++++++++--------- .../get_resources_latest_invocation.sql | 1 + 2 files changed, 17 insertions(+), 14 deletions(-) diff --git a/elementary/monitor/dbt_project/macros/get_invocations_by_ids.sql b/elementary/monitor/dbt_project/macros/get_invocations_by_ids.sql index 9b78b3ea7..000f925eb 100644 --- a/elementary/monitor/dbt_project/macros/get_invocations_by_ids.sql +++ b/elementary/monitor/dbt_project/macros/get_invocations_by_ids.sql @@ -1,19 +1,21 @@ {% macro get_invocations_by_ids(ids) %} {% set database, schema = elementary.target_database(), target.schema %} {% set invocations_relation = adapter.get_relation(database, schema, 'dbt_invocations') %} - {% if not invocations_relation %} - {% do elementary.edr_log('failed getting invocations relation') %} - {% do return(none) %} + {% if invocations_relation %} + {% set get_invocations_query %} + select + invocation_id, + command, + selected, + full_refresh, + job_url, + job_name, + job_id, + orchestrator + from {{ invocations_relation }} + where invocation_id in {{ elementary.strings_list_to_tuple(ids) }} + {% endset %} + {% set result = elementary.run_query(get_invocations_query) %} + {% do return(elementary.agate_to_dicts(result)) %} {% endif %} - - {% set get_invocations_query %} - select * from {{ invocations_relation }} where invocation_id in {{ elementary.strings_list_to_tuple(ids) }} - {% endset %} - {% set result = elementary.run_query(get_invocations_query) %} - {% if not result %} - {% do elementary.edr_log('no invocations were found') %} - {% do return(none) %} - {% endif %} - - {% do return(elementary.agate_to_dicts(result)) %} {% endmacro %} diff --git a/elementary/monitor/dbt_project/macros/get_resources_latest_invocation.sql b/elementary/monitor/dbt_project/macros/get_resources_latest_invocation.sql index 75ee2e60f..c17c22ea8 100644 --- a/elementary/monitor/dbt_project/macros/get_resources_latest_invocation.sql +++ b/elementary/monitor/dbt_project/macros/get_resources_latest_invocation.sql @@ -7,6 +7,7 @@ ROW_NUMBER() OVER (PARTITION BY unique_id ORDER BY generated_at DESC) AS row_number from {{ dbt_run_results }} ), + latest_run_results as ( select * from ordered_run_results From d74d5488ce7a0aaa264ec4f94800c7b77a8c2585 Mon Sep 17 00:00:00 2001 From: Noy Arie Date: Sun, 28 May 2023 12:03:05 +0300 Subject: [PATCH 13/16] List --- elementary/monitor/api/invocations/invocations.py | 2 +- elementary/monitor/fetchers/invocations/invocations.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/elementary/monitor/api/invocations/invocations.py b/elementary/monitor/api/invocations/invocations.py index d781d3b1d..99954084f 100644 --- a/elementary/monitor/api/invocations/invocations.py +++ b/elementary/monitor/api/invocations/invocations.py @@ -41,7 +41,7 @@ def get_invocation_by_id( raise NotImplementedError def get_invocations_by_ids( - self, invocations_ids: list[str] + self, invocations_ids: List[str] ) -> List[DbtInvocationSchema]: return self.invocations_fetcher.get_invocations_by_ids(invocations_ids) diff --git a/elementary/monitor/fetchers/invocations/invocations.py b/elementary/monitor/fetchers/invocations/invocations.py index ef95fa272..a3dd18e34 100644 --- a/elementary/monitor/fetchers/invocations/invocations.py +++ b/elementary/monitor/fetchers/invocations/invocations.py @@ -23,7 +23,7 @@ def get_test_last_invocation( return DbtInvocationSchema() def get_invocations_by_ids( - self, invocations_ids: list[str] + self, invocations_ids: List[str] ) -> List[DbtInvocationSchema]: invocations_response = self.dbt_runner.run_operation( macro_name="get_invocations_by_ids", From 9a4917e7cdee6afbcee4428d9c70e6f2193f0d82 Mon Sep 17 00:00:00 2001 From: Noy Arie Date: Mon, 29 May 2023 18:04:34 +0300 Subject: [PATCH 14/16] column_exists --- .../monitor/dbt_project/macros/get_invocations_by_ids.sql | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/elementary/monitor/dbt_project/macros/get_invocations_by_ids.sql b/elementary/monitor/dbt_project/macros/get_invocations_by_ids.sql index 000f925eb..ae407e565 100644 --- a/elementary/monitor/dbt_project/macros/get_invocations_by_ids.sql +++ b/elementary/monitor/dbt_project/macros/get_invocations_by_ids.sql @@ -1,6 +1,8 @@ {% macro get_invocations_by_ids(ids) %} {% set database, schema = elementary.target_database(), target.schema %} {% set invocations_relation = adapter.get_relation(database, schema, 'dbt_invocations') %} + {% set column_exists = elementary.column_exists_in_relation(invocations_relation, 'job_url') %} + {% if invocations_relation %} {% set get_invocations_query %} select @@ -8,7 +10,9 @@ command, selected, full_refresh, - job_url, + {% if column_exists %} + job_url, + {% endif %} job_name, job_id, orchestrator From bc4044fbdc989bd034bf886c8c72c5a52b1af988 Mon Sep 17 00:00:00 2001 From: Noy Arie Date: Wed, 31 May 2023 11:08:38 +0300 Subject: [PATCH 15/16] update json file for tests --- tests/e2e/report/fixtures/elementary_output.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/e2e/report/fixtures/elementary_output.json b/tests/e2e/report/fixtures/elementary_output.json index 7d850737f..1d4fa871f 100644 --- a/tests/e2e/report/fixtures/elementary_output.json +++ b/tests/e2e/report/fixtures/elementary_output.json @@ -1 +1 @@ -{"creation_time": "2023-01-02T10:48:01+00:00", "days_back": 7, "models": {"model.elementary_integration_tests.stats_team": {"name": "stats_team", "unique_id": "model.elementary_integration_tests.stats_team", "owners": [], "tags": [], "package_name": "elementary_integration_tests", "description": "", "full_path": "models/stats_team.sql", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "stats_team", "model_name": "stats_team", "normalized_full_path": "elementary_integration_tests/models/stats_team.sql"}, "model.elementary_integration_tests.dimension_anomalies": {"name": "dimension_anomalies", "unique_id": "model.elementary_integration_tests.dimension_anomalies", "owners": ["@egk.com"], "tags": [], "package_name": "elementary_integration_tests", "description": "We use this model to test dimension anomalies", "full_path": "models/dimension_anomalies.sql", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "dimension_anomalies", "model_name": "dimension_anomalies", "normalized_full_path": "elementary_integration_tests/models/dimension_anomalies.sql"}, "model.elementary_integration_tests.stats_players": {"name": "stats_players", "unique_id": "model.elementary_integration_tests.stats_players", "owners": [], "tags": [], "package_name": "elementary_integration_tests", "description": "", "full_path": "models/stats_players.sql", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "stats_players", "model_name": "stats_players", "normalized_full_path": "elementary_integration_tests/models/stats_players.sql"}, "model.elementary_integration_tests.groups": {"name": "groups", "unique_id": "model.elementary_integration_tests.groups", "owners": [], "tags": [], "package_name": "elementary_integration_tests", "description": "", "full_path": "models/groups.sql", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "groups", "model_name": "groups", "normalized_full_path": "elementary_integration_tests/models/groups.sql"}, "model.elementary_integration_tests.no_timestamp_anomalies": {"name": "no_timestamp_anomalies", "unique_id": "model.elementary_integration_tests.no_timestamp_anomalies", "owners": ["ele@data.com", "another@mail.com"], "tags": [], "package_name": "elementary_integration_tests", "description": "We use this model to test anomalies when there is no timestamp column", "full_path": "models/no_timestamp_anomalies.sql", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "no_timestamp_anomalies", "model_name": "no_timestamp_anomalies", "normalized_full_path": "elementary_integration_tests/models/no_timestamp_anomalies.sql"}, "model.elementary_integration_tests.numeric_column_anomalies": {"name": "numeric_column_anomalies", "unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "owners": [], "tags": [], "package_name": "elementary_integration_tests", "description": "", "full_path": "models/numeric_column_anomalies.sql", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "model_name": "numeric_column_anomalies", "normalized_full_path": "elementary_integration_tests/models/numeric_column_anomalies.sql"}, "model.elementary_integration_tests.string_column_anomalies": {"name": "string_column_anomalies", "unique_id": "model.elementary_integration_tests.string_column_anomalies", "owners": ["@or"], "tags": ["marketing"], "package_name": "elementary_integration_tests", "description": "", "full_path": "models/string_column_anomalies.sql", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "string_column_anomalies", "model_name": "string_column_anomalies", "normalized_full_path": "elementary_integration_tests/models/string_column_anomalies.sql"}, "model.elementary_integration_tests.any_type_column_anomalies": {"name": "any_type_column_anomalies", "unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "owners": ["@edr", "ele@data.com", "another@mail.com"], "tags": [], "package_name": "elementary_integration_tests", "description": "This is a very weird description with breaklines and comma, and even a string like this 'wow'. You know, these $##$34#@#!^ can also be helpful WDYT?\n", "full_path": "models/any_type_column_anomalies.sql", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "model_name": "any_type_column_anomalies", "normalized_full_path": "elementary_integration_tests/models/any_type_column_anomalies.sql"}, "model.elementary_integration_tests.error_model": {"name": "error_model", "unique_id": "model.elementary_integration_tests.error_model", "owners": [], "tags": ["error_model"], "package_name": "elementary_integration_tests", "description": "We use this model to create error runs and tests", "full_path": "models/error_model.sql", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "error_model", "model_name": "error_model", "normalized_full_path": "elementary_integration_tests/models/error_model.sql"}, "model.elementary_integration_tests.nested": {"name": "nested", "unique_id": "model.elementary_integration_tests.nested", "owners": [], "tags": [], "package_name": "elementary_integration_tests", "description": "", "full_path": "models/nested/models/tree/nested.sql", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "nested", "model_name": "nested", "normalized_full_path": "elementary_integration_tests/models/nested/models/tree/nested.sql"}, "source.elementary_integration_tests.training.any_type_column_anomalies_training": {"name": "any_type_column_anomalies_training", "unique_id": "source.elementary_integration_tests.training.any_type_column_anomalies_training", "owners": [], "tags": [], "package_name": "elementary_integration_tests", "description": "", "full_path": "models/schema.yml", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_training", "model_name": "any_type_column_anomalies_training", "normalized_full_path": "elementary_integration_tests/sources/schema.sql"}, "source.elementary_integration_tests.training.string_column_anomalies_training": {"name": "string_column_anomalies_training", "unique_id": "source.elementary_integration_tests.training.string_column_anomalies_training", "owners": [], "tags": [], "package_name": "elementary_integration_tests", "description": "", "full_path": "models/schema.yml", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "string_column_anomalies_training", "model_name": "string_column_anomalies_training", "normalized_full_path": "elementary_integration_tests/sources/schema.sql"}, "source.elementary_integration_tests.training.numeric_column_anomalies_training": {"name": "numeric_column_anomalies_training", "unique_id": "source.elementary_integration_tests.training.numeric_column_anomalies_training", "owners": [], "tags": [], "package_name": "elementary_integration_tests", "description": "", "full_path": "models/schema.yml", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "numeric_column_anomalies_training", "model_name": "numeric_column_anomalies_training", "normalized_full_path": "elementary_integration_tests/sources/schema.sql"}, "source.elementary_integration_tests.validation.any_type_column_anomalies_validation": {"name": "any_type_column_anomalies_validation", "unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "owners": [], "tags": [], "package_name": "elementary_integration_tests", "description": "", "full_path": "models/schema.yml", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "model_name": "any_type_column_anomalies_validation", "normalized_full_path": "elementary_integration_tests/sources/schema.sql"}, "exposure.elementary_integration_tests.elementary_exposure": {"name": "elementary_exposure", "unique_id": "exposure.elementary_integration_tests.elementary_exposure", "owners": ["Complete Nonsense"], "tags": [], "package_name": "elementary_integration_tests", "description": "Keep calm, Elementary tests exposures.\n", "full_path": "models/schema.yml", "url": "https://elementary.not.really", "type": "application", "maturity": "medium", "owner_email": "fake@fakerson.com", "model_name": "elementary_exposure", "normalized_full_path": "elementary_integration_tests/models/schema.yml"}, "exposure.elementary_integration_tests.weekly_jaffle_metrics": {"name": "weekly_jaffle_metrics", "unique_id": "exposure.elementary_integration_tests.weekly_jaffle_metrics", "owners": ["Claire from Data"], "tags": [], "package_name": "elementary_integration_tests", "description": "Did someone say \"exponential growth\"?\n", "full_path": "models/schema.yml", "url": "https://bi.tool/dashboards/1", "type": "dashboard", "maturity": "high", "owner_email": "data@jaffleshop.com", "model_name": "weekly_jaffle_metrics", "normalized_full_path": "elementary_integration_tests/models/schema.yml"}}, "sidebars": {"dbt": {"elementary_integration_tests": {"models": {"__files__": ["model.elementary_integration_tests.stats_team", "model.elementary_integration_tests.dimension_anomalies", "model.elementary_integration_tests.stats_players", "model.elementary_integration_tests.groups", "model.elementary_integration_tests.no_timestamp_anomalies", "model.elementary_integration_tests.numeric_column_anomalies", "model.elementary_integration_tests.string_column_anomalies", "model.elementary_integration_tests.any_type_column_anomalies", "model.elementary_integration_tests.error_model"], "nested": {"models": {"tree": {"__files__": ["model.elementary_integration_tests.nested"]}}}}, "sources": {"__files__": ["source.elementary_integration_tests.training.any_type_column_anomalies_training", "source.elementary_integration_tests.training.string_column_anomalies_training", "source.elementary_integration_tests.training.numeric_column_anomalies_training", "source.elementary_integration_tests.validation.any_type_column_anomalies_validation"]}}}, "tags": {"No tags": ["model.elementary_integration_tests.stats_team", "model.elementary_integration_tests.dimension_anomalies", "model.elementary_integration_tests.stats_players", "model.elementary_integration_tests.groups", "model.elementary_integration_tests.no_timestamp_anomalies", "model.elementary_integration_tests.numeric_column_anomalies", "model.elementary_integration_tests.any_type_column_anomalies", "model.elementary_integration_tests.nested", "source.elementary_integration_tests.training.any_type_column_anomalies_training", "source.elementary_integration_tests.training.string_column_anomalies_training", "source.elementary_integration_tests.training.numeric_column_anomalies_training", "source.elementary_integration_tests.validation.any_type_column_anomalies_validation"], "marketing": ["model.elementary_integration_tests.string_column_anomalies"], "error_model": ["model.elementary_integration_tests.error_model"]}, "owners": {"No owners": ["model.elementary_integration_tests.stats_team", "model.elementary_integration_tests.stats_players", "model.elementary_integration_tests.groups", "model.elementary_integration_tests.numeric_column_anomalies", "model.elementary_integration_tests.error_model", "model.elementary_integration_tests.nested", "source.elementary_integration_tests.training.any_type_column_anomalies_training", "source.elementary_integration_tests.training.string_column_anomalies_training", "source.elementary_integration_tests.training.numeric_column_anomalies_training", "source.elementary_integration_tests.validation.any_type_column_anomalies_validation"], "@egk.com": ["model.elementary_integration_tests.dimension_anomalies"], "ele@data.com": ["model.elementary_integration_tests.no_timestamp_anomalies", "model.elementary_integration_tests.any_type_column_anomalies"], "another@mail.com": ["model.elementary_integration_tests.no_timestamp_anomalies", "model.elementary_integration_tests.any_type_column_anomalies"], "@or": ["model.elementary_integration_tests.string_column_anomalies"], "@edr": ["model.elementary_integration_tests.any_type_column_anomalies"]}}, "invocation": {"invocation_id": null, "detected_at": null, "command": null, "selected": null, "full_refresh": null}, "test_results": {"model.elementary_integration_tests.numeric_column_anomalies": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": "ZERO_PERCENT", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:24+02:00", "latest_run_time_utc": "2023-01-02T10:44:24+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "min", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_zero_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('min' as varchar)\n and upper(column_name) = upper(cast('ZERO_PERCENT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column ZERO_PERCENT, the last min value is 0. The average for this metric is 0.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_zero_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('min' as varchar)\n and upper(column_name) = upper(cast('ZERO_PERCENT' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Min", "metrics": [{"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "4df1e3096b8159e9415e4f2e08601b0b", "metric_id": "e682d6eb2ccad9c9116f74a6b7481e3c", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last min value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "5cf63c52abdd792fc7476ceceb5f8ff1", "metric_id": "6060a7e3154e5a0fb54349a6e1e9e583", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last min value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "1a6bbbed3b6b3275f7cfb3803a376e61", "metric_id": "edd3571117df20d4b281b8a551b967bc", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last min value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "269b464e11bf3ec4f83bb22097c7eecb", "metric_id": "3716b55eadfb4623ce8e376e64da1ab3", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last min value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "ba78c9e42053a325142fb9458b7f9615", "metric_id": "83eb1b114ec76c12ce12919582bdd48a", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last min value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "6f370047843fb0a7c6c6352e5b86057c", "metric_id": "994094986ca7e5a1f9d7dafdbc94be85", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last min value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "42bb23fc460cd05cf97409688e260482", "metric_id": "df177c8fe6ff2d7f49456fc35f94764a", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last min value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "23dbe25c06994dd8e73cd562af899598", "metric_id": "480fa87355de9b3438c549422d0b6445", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last min value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "380d72136b36930e82219475fa0c58d9", "metric_id": "8f388422ab41aa5467f583690d00f515", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last min value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "d7ccb4aa8fdfc4bfd67416a506253639", "metric_id": "1635dada623e2715cfa054a239e4e836", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last min value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "737ef6f3d959f2c5aef4b02c988bca9b", "metric_id": "8e7d2c29d5e6e82a1278ba0bad152755", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last min value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "69acd6c9ca373f313116d41710dddde3", "metric_id": "d184bf7b679cf7a1ea775737414ac907", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last min value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "acfe7f09923840f2fa7dce5943760a03", "metric_id": "5b21287ef1c44c00d56e145d58e8e687", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last min value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "bcf94fc8f725ff93e04725d477f60a9c", "metric_id": "7e58e01de324f6ac490946466955d52d", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last min value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "33766c8d5eaf16289ea6d64452c3e2a1", "metric_id": "5c3a327360011f069aa58aa87f4d6cc0", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last min value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "01fb70036f38c0eabff326f4c0730389", "metric_id": "ed2c8a44c0bb17bcf5a953fd3fa1122d", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last min value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "97db2314dd6c6024d39e28f852e07bd7", "metric_id": "1af59a1851d662ae54a3fc08a209de15", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last min value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "1060427b56438c3e7baa1aba0c9b741b", "metric_id": "68ff3ce2c9653e21b16d5c1cbeb5a8b9", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last min value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "70b9c0adc2dc06d478fd4cdf94a0dc82", "metric_id": "07e192a73c8b44ff1727b57fe47f10ae", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last min value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "0f503560a63d2c937df53acf90bd4cfe", "metric_id": "618fc0b2a38c1ea1af9d2da8045e5a2b", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last min value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "6baadd7a99587347fbb959b0ce1b13d6", "metric_id": "89f2398d4eccd9dc5e895d499584e422", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last min value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "e1c696b2759b76459725cb783e34c32f", "metric_id": "aabf30c9b758e1ab77e0d696b58f970f", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last min value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "aab4127b126ffc4f87dfc828f813bcfe", "metric_id": "60e9991a77cb1b654954d1b31c75cf30", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last min value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "f59d003ed4e498d8fa74ed96112cbc3f", "metric_id": "e20bad5acf7543b947a0f63d33be7e88", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last min value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "c02a6f0e55056aec244cd37363a3f219", "metric_id": "0b524ac278dd477539331138270060e9", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last min value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "44f394bcc75043a40171cef59e5f52e6", "metric_id": "22150fc32dee5ae00222f9dc555a71ee", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last min value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "317efdb57a2a6600fc7deb912820cc56", "metric_id": "1900546d0570d5a2ae1166ef29229b85", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last min value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "f1eade623aae098519fdcc8ffe0094f5", "metric_id": "8b561ef434a96a42d187d3db7aed795f", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last min value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "ceaa828a2263206b678dded5cd2e6cca", "metric_id": "665b07ed68afbd48ba7320d910af3c78", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last min value is 0. The average for this metric is 0.", "is_anomalous": false}], "result_description": "In column ZERO_PERCENT, the last min value is 0. The average for this metric is 0."}}, {"metadata": {"test_unique_id": "model.elementary_integration_tests.numeric_column_anomalies.elementary_freshness_anomalies_numeric_column_anomalies_.freshness_anomalies", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": null, "test_name": "freshness_anomalies", "test_display_name": "Freshness Anomalies", "latest_run_time": "2023-01-02T12:42:19+02:00", "latest_run_time_utc": "2023-01-02T10:42:19+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "freshness", "test_query": "select * from (\n \n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_FRESHNESS_ANOMALIES_NUMERIC_COLUMN_ANOMALIES___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n\n \n\n\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('freshness' as varchar)", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "Last update was at 1969-12-30 00:00:00.000, 48 hours ago. Usually the table is updated within 24.8 hours.", "result_query": "select * from (\n \n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_FRESHNESS_ANOMALIES_NUMERIC_COLUMN_ANOMALIES___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n\n \n\n\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('freshness' as varchar)"}, "configuration": {"test_name": "freshness_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Freshness", "metrics": [{"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "70e70a836567c65a7fbb57ab2852f385", "metric_id": "e8687629e2f9ed71b7a93cdf30a7c836", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "detected_at": "2023-01-02T10:42:18.970000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-03 00:00:00.000", "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-03 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "c10cbd29c4f304c3a622ebe968c273ef", "metric_id": "63a33360e1b13ca6bf430a2011a49ea7", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "detected_at": "2023-01-02T10:42:18.970000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-04 00:00:00.000", "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-04 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "dd799c94998e2d247af596ada5764a02", "metric_id": "9ba49d4aac3471e455dfd46e3309ab3a", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "detected_at": "2023-01-02T10:42:18.970000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-05 00:00:00.000", "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-05 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "fa8c19b88f862faec84c36b64aacfbe0", "metric_id": "c99ce333c9f7585ca9da90a886cee65b", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "detected_at": "2023-01-02T10:42:18.970000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-06 00:00:00.000", "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-06 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "15bb9d0309cd6335b433b7cca935ded7", "metric_id": "78d1a3c260fdb3b722c942103a54c85f", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "detected_at": "2023-01-02T10:42:18.970000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-07 00:00:00.000", "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-07 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "66369e00bfb22a5dc6e352bb6fd11442", "metric_id": "6a9d4dfc1718213eb74dae705c53eab6", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "detected_at": "2023-01-02T10:42:18.970000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-08 00:00:00.000", "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-08 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "25938ed422c1f660172a3bfc6b356c99", "metric_id": "02e2a7cf5596495b49ea1e1d50433a96", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "detected_at": "2023-01-02T10:42:18.970000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-09 00:00:00.000", "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-09 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "a2c79bf2d51690d1b298236d5286d016", "metric_id": "2bd71d16c7f1a6e8215aef95e39bfc48", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "detected_at": "2023-01-02T10:42:18.970000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-10 00:00:00.000", "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-10 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "89aa5897fbfb2af1235668fcfac4a9f4", "metric_id": "54bab3f7a4cf0729153459ce6604a717", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "detected_at": "2023-01-02T10:42:18.970000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-11 00:00:00.000", "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-11 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "c3d505bda7f742f17146d47e8c22e8f4", "metric_id": "66b02bb185b23126764c30780027856b", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "detected_at": "2023-01-02T10:42:18.970000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-12 00:00:00.000", "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-12 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "4603363fb34ae8165246a506e23c3712", "metric_id": "eb2d78bbae6ffa3596c8e9cdbd0301d5", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "detected_at": "2023-01-02T10:42:18.970000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-13 00:00:00.000", "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-13 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "2bc23fe886d77c2c43c04e543b387f2c", "metric_id": "f5f436c96a019bdc560133ff2c0a14f6", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "detected_at": "2023-01-02T10:42:18.970000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-14 00:00:00.000", "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-14 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "0577b28ecb1dd07d093094ff1950a105", "metric_id": "66acdf875d988bdec2564948491a41cb", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "detected_at": "2023-01-02T10:42:18.970000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-15 00:00:00.000", "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-15 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "29348f7ea2358f3784fac39270f48838", "metric_id": "ee430d0b5b0f17a811e00f704944d2d0", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "detected_at": "2023-01-02T10:42:18.970000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-16 00:00:00.000", "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-16 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "4feb26fc0eaa6c4036e6f10b6bf72f9e", "metric_id": "3b0df7d3d027e9ea5e7af0149aac3056", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "detected_at": "2023-01-02T10:42:18.970000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-17 00:00:00.000", "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-17 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "45f7654a3e4e57ce170577228dd27af8", "metric_id": "f0c9c3e34038e1b29d31a5deb7144e9c", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "detected_at": "2023-01-02T10:42:18.970000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-18 00:00:00.000", "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-18 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "921a604d1784ba566a6765578eca0f2a", "metric_id": "12b8a69d30909e382fec628f295c8c08", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "detected_at": "2023-01-02T10:42:18.970000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-19 00:00:00.000", "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-19 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "2be39840b93786342bf839a39e9c462c", "metric_id": "b05feded35dbc851718e5402e8a2084c", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "detected_at": "2023-01-02T10:42:18.970000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-20 00:00:00.000", "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-20 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "396afbedb92bb76650d6926ffd8d357e", "metric_id": "ee8d47f71356c5d07ddb89d15781171f", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "detected_at": "2023-01-02T10:42:18.970000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-21 00:00:00.000", "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-21 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "083fd9720834cfbc8353931f9b21fc74", "metric_id": "8f05bbb8b54a28d4c4730836336c52b9", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "detected_at": "2023-01-02T10:42:18.970000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-22 00:00:00.000", "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-22 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "e4acee54e473e2efbc9aa5ab1fb6cb8b", "metric_id": "8a107993c5827dc0c1ab89e4f8e25ca3", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "detected_at": "2023-01-02T10:42:18.970000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-23 00:00:00.000", "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-23 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "44d224891b323f0ee907ee0bb211d7df", "metric_id": "d31ab3fc4a9e1bdae436dde5ebc1eeab", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "detected_at": "2023-01-02T10:42:18.970000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-24 00:00:00.000", "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-24 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "0caa01adc5fb29958b2e6958254cdd98", "metric_id": "2e43f9131212e89091b2dd45d428ffd8", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "detected_at": "2023-01-02T10:42:18.970000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-25 00:00:00.000", "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-25 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "e4943ea1a751db01d090ff44efa94fd7", "metric_id": "934dfaeedcefb8b2b510b9ee7cd11f42", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "detected_at": "2023-01-02T10:42:18.970000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-26 00:00:00.000", "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-26 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "069aabf6159e65a31d7376ea5dde7dda", "metric_id": "f35f127b50d9f2ecf0b4c0e0c68a360a", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "detected_at": "2023-01-02T10:42:18.970000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-27 00:00:00.000", "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-27 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "3f09333e79cbd2f7bd2e87c9253ac1fe", "metric_id": "df480f0881767e11aeb7a7fadd2e4457", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "detected_at": "2023-01-02T10:42:18.970000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-28 00:00:00.000", "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-28 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "b3c2e037e0df6dc3a5479d46b50ba560", "metric_id": "6ab71cf55a1c1264e69fc7307b95feef", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "detected_at": "2023-01-02T10:42:18.970000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-29 00:00:00.000", "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-29 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "3898005896fd92c2db6c82abd990396d", "metric_id": "e23290cf888a2ebf0191ff1d2133c602", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "detected_at": "2023-01-02T10:42:18.970000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-30 00:00:00.000", "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-30 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 172800.0, "average": 89280.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "7b8b9df7cbe60e64ea7963ad83b2526d", "metric_id": "04d9a0329e9574114423b0964a888e7d", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "detected_at": "2023-01-02T10:42:18.970000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 5.294651389, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-30 00:00:00.000", "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 172800.0, "min_metric_value": 41956.771031554, "max_metric_value": 136603.228968446, "training_avg": 89280.0, "training_stddev": 15774.409656149, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-30 00:00:00.000, 48 hours ago. Usually the table is updated within 24.8 hours.", "is_anomalous": true}], "result_description": "Last update was at 1969-12-30 00:00:00.000, 48 hours ago. Usually the table is updated within 24.8 hours."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": "MAX", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:22+02:00", "latest_run_time_utc": "2023-01-02T10:44:22+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "average", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_average__max__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('MAX' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column MAX, the last average value is 204.19. The average for this metric is 152.37.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_average__max__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('MAX' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Average", "metrics": [{"value": 149.54, "average": 149.9425, "min_value": 148.234837123, "max_value": 151.650162877, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "bee9e26871b1b31193b31e88b5b2f3bf", "metric_id": "a0463fad1c4ce4716890fe99934b5069", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "detected_at": "2023-01-02T10:44:21.905000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MAX", "metric_name": "average", "anomaly_score": -0.7071067812, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 149.54, "min_metric_value": 148.234837123, "max_metric_value": 151.650162877, "training_avg": 149.9425, "training_stddev": 0.5692209589, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MAX, the last average value is 149.54. The average for this metric is 149.943.", "is_anomalous": false}, {"value": 150.14, "average": 150.008333333, "min_value": 148.753313413, "max_value": 151.263353254, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "6ce9e89c81b6011dc8f73c7a98c0287f", "metric_id": "f231cf359f2bbfe7badb80d298670ea0", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "detected_at": "2023-01-02T10:44:21.905000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MAX", "metric_name": "average", "anomaly_score": 0.3147360402, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 150.14, "min_metric_value": 148.753313413, "max_metric_value": 151.263353254, "training_avg": 150.008333333, "training_stddev": 0.4183399734, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MAX, the last average value is 150.14. The average for this metric is 150.008.", "is_anomalous": false}, {"value": 149.165, "average": 149.7975, "min_value": 148.169533477, "max_value": 151.425466523, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "d7a7a9c97cc45f2dba777dc5937da87c", "metric_id": "7c29572ddc5b8640f992f7b9edd4d3d4", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "detected_at": "2023-01-02T10:44:21.905000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MAX", "metric_name": "average", "anomaly_score": -1.165564508, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 149.165, "min_metric_value": 148.169533477, "max_metric_value": 151.425466523, "training_avg": 149.7975, "training_stddev": 0.5426555077, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MAX, the last average value is 149.165. The average for this metric is 149.797.", "is_anomalous": false}, {"value": 148.92, "average": 149.622, "min_value": 147.785231778, "max_value": 151.458768222, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "126ace63bde2562dee6356e5ea42ac8e", "metric_id": "e1ad4cf85165af8cdce7891a0f566520", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "detected_at": "2023-01-02T10:44:21.905000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MAX", "metric_name": "average", "anomaly_score": -1.14657907, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 148.92, "min_metric_value": 147.785231778, "max_metric_value": 151.458768222, "training_avg": 149.622, "training_stddev": 0.6122560739, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MAX, the last average value is 148.92. The average for this metric is 149.622.", "is_anomalous": false}, {"value": 149.03, "average": 149.523333333, "min_value": 147.727596716, "max_value": 151.319069951, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "248bd3a32b16a58c8c45e57a200ee4bc", "metric_id": "d76fea6560fbe7d4886bd0a4b56fafa4", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "detected_at": "2023-01-02T10:44:21.905000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MAX", "metric_name": "average", "anomaly_score": -0.8241743168, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 149.03, "min_metric_value": 147.727596716, "max_metric_value": 151.319069951, "training_avg": 149.523333333, "training_stddev": 0.5985788726, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MAX, the last average value is 149.03. The average for this metric is 149.523.", "is_anomalous": false}, {"value": 151.445, "average": 149.797857143, "min_value": 147.071116545, "max_value": 152.52459774, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "b1594b12fe37e63c6ac83630fe12ea08", "metric_id": "c8eddc93a041420cff6d596273e3d810", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "detected_at": "2023-01-02T10:44:21.905000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MAX", "metric_name": "average", "anomaly_score": 1.812210731, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 151.445, "min_metric_value": 147.071116545, "max_metric_value": 152.52459774, "training_avg": 149.797857143, "training_stddev": 0.9089135325, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MAX, the last average value is 151.445. The average for this metric is 149.798.", "is_anomalous": false}, {"value": 153.635, "average": 150.2775, "min_value": 145.488232982, "max_value": 155.066767018, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "06ab14ac0d1b5daeeba24b8bad8ccf50", "metric_id": "eb6ce35114168d96e1b1cb53bf6fe21f", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "detected_at": "2023-01-02T10:44:21.905000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MAX", "metric_name": "average", "anomaly_score": 2.103140201, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 153.635, "min_metric_value": 145.488232982, "max_metric_value": 155.066767018, "training_avg": 150.2775, "training_stddev": 1.596422339, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MAX, the last average value is 153.635. The average for this metric is 150.277.", "is_anomalous": false}, {"value": 152.955, "average": 150.575, "min_value": 145.355905251, "max_value": 155.794094749, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "bdea74832f2dbc846fbeab5d06cfd8c0", "metric_id": "55cb76e3e1bdd8d897a95d7707ae233c", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "detected_at": "2023-01-02T10:44:21.905000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MAX", "metric_name": "average", "anomaly_score": 1.368053339, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 152.955, "min_metric_value": 145.355905251, "max_metric_value": 155.794094749, "training_avg": 150.575, "training_stddev": 1.73969825, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MAX, the last average value is 152.955. The average for this metric is 150.575.", "is_anomalous": false}, {"value": 151.08, "average": 150.6255, "min_value": 145.681622726, "max_value": 155.569377274, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "a17c64592f25700b277b216ee0aea210", "metric_id": "f3b27655324f67f3bf8290d35afdcc3a", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "detected_at": "2023-01-02T10:44:21.905000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MAX", "metric_name": "average", "anomaly_score": 0.2757956811, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 151.08, "min_metric_value": 145.681622726, "max_metric_value": 155.569377274, "training_avg": 150.6255, "training_stddev": 1.647959091, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MAX, the last average value is 151.08. The average for this metric is 150.625.", "is_anomalous": false}, {"value": 151.175, "average": 150.675454545, "min_value": 145.959017283, "max_value": 155.391891808, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "eaaa3fc286d9f68f3d83cfa0f7b82414", "metric_id": "acab381429432b1c79bb7c0efa411e86", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "detected_at": "2023-01-02T10:44:21.905000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MAX", "metric_name": "average", "anomaly_score": 0.3177475455, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 151.175, "min_metric_value": 145.959017283, "max_metric_value": 155.391891808, "training_avg": 150.675454545, "training_stddev": 1.572145754, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MAX, the last average value is 151.175. The average for this metric is 150.675.", "is_anomalous": false}, {"value": 151.395, "average": 150.735416667, "min_value": 146.195500706, "max_value": 155.275332628, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "3abe7b54d04cde9a3444edf5d279f7d5", "metric_id": "0739f12907d8f501476250523a79b274", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "detected_at": "2023-01-02T10:44:21.905000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MAX", "metric_name": "average", "anomaly_score": 0.4358560857, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 151.395, "min_metric_value": 146.195500706, "max_metric_value": 155.275332628, "training_avg": 150.735416667, "training_stddev": 1.51330532, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MAX, the last average value is 151.395. The average for this metric is 150.735.", "is_anomalous": false}, {"value": 147.5, "average": 150.486538462, "min_value": 145.373780945, "max_value": 155.599295978, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "a08004d0ff012cf9e8cb884361358973", "metric_id": "98bc2bac1bb198bf4d3f1f22f69257c3", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "detected_at": "2023-01-02T10:44:21.905000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MAX", "metric_name": "average", "anomaly_score": -1.752403738, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 147.5, "min_metric_value": 145.373780945, "max_metric_value": 155.599295978, "training_avg": 150.486538462, "training_stddev": 1.704252506, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MAX, the last average value is 147.5. The average for this metric is 150.487.", "is_anomalous": false}, {"value": 152.455, "average": 150.627142857, "min_value": 145.467640262, "max_value": 155.786645453, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "d24c87acf23432d59cc864df67b3b3d3", "metric_id": "071e0f60957ffcd2a51ef6cbf901d775", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "detected_at": "2023-01-02T10:44:21.905000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MAX", "metric_name": "average", "anomaly_score": 1.062810092, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 152.455, "min_metric_value": 145.467640262, "max_metric_value": 155.786645453, "training_avg": 150.627142857, "training_stddev": 1.719834199, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MAX, the last average value is 152.455. The average for this metric is 150.627.", "is_anomalous": false}, {"value": 150.93, "average": 150.647333333, "min_value": 145.669980767, "max_value": 155.6246859, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "1e32ead5d259ace7b42dae6f1e9ddc8a", "metric_id": "267a064f6dab05109b6f53d5519b35e8", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "detected_at": "2023-01-02T10:44:21.905000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MAX", "metric_name": "average", "anomaly_score": 0.1703716963, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 150.93, "min_metric_value": 145.669980767, "max_metric_value": 155.6246859, "training_avg": 150.647333333, "training_stddev": 1.659117522, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MAX, the last average value is 150.93. The average for this metric is 150.647.", "is_anomalous": false}, {"value": 150.86, "average": 150.660625, "min_value": 145.849401013, "max_value": 155.471848987, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "e743e94258ae97b31a9408576935a564", "metric_id": "6b39145ca21f9e455ef651418e649ed9", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "detected_at": "2023-01-02T10:44:21.905000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MAX", "metric_name": "average", "anomaly_score": 0.1243186768, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 150.86, "min_metric_value": 145.849401013, "max_metric_value": 155.471848987, "training_avg": 150.660625, "training_stddev": 1.603741329, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MAX, the last average value is 150.86. The average for this metric is 150.661.", "is_anomalous": false}, {"value": 152.68, "average": 150.779411765, "min_value": 145.894740927, "max_value": 155.664082602, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "ee26e55ac31ddf4610a26b3a1d35cf39", "metric_id": "ddedf46d94cb2a371ad8911d98889f12", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "detected_at": "2023-01-02T10:44:21.905000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MAX", "metric_name": "average", "anomaly_score": 1.167277161, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 152.68, "min_metric_value": 145.894740927, "max_metric_value": 155.664082602, "training_avg": 150.779411765, "training_stddev": 1.628223613, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MAX, the last average value is 152.68. The average for this metric is 150.779.", "is_anomalous": false}, {"value": 155.955, "average": 151.066944444, "min_value": 145.079471185, "max_value": 157.054417704, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "bece2c23d6d8073db5008e40be1762a8", "metric_id": "6da5aeaec40e82b8c2a68ebeb9ec17fa", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "detected_at": "2023-01-02T10:44:21.905000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MAX", "metric_name": "average", "anomaly_score": 2.44914107, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 155.955, "min_metric_value": 145.079471185, "max_metric_value": 157.054417704, "training_avg": 151.066944444, "training_stddev": 1.99582442, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MAX, the last average value is 155.955. The average for this metric is 151.067.", "is_anomalous": false}, {"value": 150.06, "average": 151.013947368, "min_value": 145.154044257, "max_value": 156.87385048, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "ff67b1df28233b399c01cac55bd2069b", "metric_id": "6698b2cabe1ef00e1a246a924f8d7e43", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "detected_at": "2023-01-02T10:44:21.905000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MAX", "metric_name": "average", "anomaly_score": -0.4883770347, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 150.06, "min_metric_value": 145.154044257, "max_metric_value": 156.87385048, "training_avg": 151.013947368, "training_stddev": 1.953301037, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MAX, the last average value is 150.06. The average for this metric is 151.014.", "is_anomalous": false}, {"value": 149.565, "average": 150.9415, "min_value": 145.155661367, "max_value": 156.727338633, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "5412f2059f1ad2e4c43d2235c4a3d5f2", "metric_id": "44d00fa0aae70b0fd89e4d0d68026fac", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "detected_at": "2023-01-02T10:44:21.905000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MAX", "metric_name": "average", "anomaly_score": -0.7137254013, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 149.565, "min_metric_value": 145.155661367, "max_metric_value": 156.727338633, "training_avg": 150.9415, "training_stddev": 1.928612878, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MAX, the last average value is 149.565. The average for this metric is 150.941.", "is_anomalous": false}, {"value": 145.355, "average": 150.67547619, "min_value": 143.954061878, "max_value": 157.396890503, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "a2d8356e7455e238393a2d7e04cf90ab", "metric_id": "0884a19f7ae0230d776604c6e8f615f5", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "detected_at": "2023-01-02T10:44:21.905000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MAX", "metric_name": "average", "anomaly_score": -2.374712796, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 145.355, "min_metric_value": 143.954061878, "max_metric_value": 157.396890503, "training_avg": 150.67547619, "training_stddev": 2.240471437, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MAX, the last average value is 145.355. The average for this metric is 150.675.", "is_anomalous": false}, {"value": 151.565, "average": 150.715909091, "min_value": 144.13185264, "max_value": 157.299965542, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "fd08e6253b8dfa56d0064cf42d5e6b2a", "metric_id": "75dc96c29405d6f17d11586352571743", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "detected_at": "2023-01-02T10:44:21.905000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MAX", "metric_name": "average", "anomaly_score": 0.3868850072, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 151.565, "min_metric_value": 144.13185264, "max_metric_value": 157.299965542, "training_avg": 150.715909091, "training_stddev": 2.194685484, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MAX, the last average value is 151.565. The average for this metric is 150.716.", "is_anomalous": false}, {"value": 151.615, "average": 150.755, "min_value": 144.297781523, "max_value": 157.212218477, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "5e08aab77e074ed9a3ba9d78e7fd2490", "metric_id": "e0056aef830abd962bec1ce81dba293b", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "detected_at": "2023-01-02T10:44:21.905000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MAX", "metric_name": "average", "anomaly_score": 0.399552843, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 151.615, "min_metric_value": 144.297781523, "max_metric_value": 157.212218477, "training_avg": 150.755, "training_stddev": 2.152406159, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MAX, the last average value is 151.615. The average for this metric is 150.755.", "is_anomalous": false}, {"value": 152.03, "average": 150.808125, "min_value": 144.444759177, "max_value": 157.171490823, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "bda7a7f57d157f643cb68011c82268e3", "metric_id": "7233020e562e868bd56e1cea8202169a", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "detected_at": "2023-01-02T10:44:21.905000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MAX", "metric_name": "average", "anomaly_score": 0.5760512757, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 152.03, "min_metric_value": 144.444759177, "max_metric_value": 157.171490823, "training_avg": 150.808125, "training_stddev": 2.121121941, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MAX, the last average value is 152.03. The average for this metric is 150.808.", "is_anomalous": false}, {"value": 150.665, "average": 150.8024, "min_value": 144.572422893, "max_value": 157.032377107, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "8c004ee2cbf1fbb2e77656b0fc61fd22", "metric_id": "dc96c4f44cd819fb27a6ee1a23e087d3", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "detected_at": "2023-01-02T10:44:21.905000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MAX", "metric_name": "average", "anomaly_score": -0.06616396705, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 150.665, "min_metric_value": 144.572422893, "max_metric_value": 157.032377107, "training_avg": 150.8024, "training_stddev": 2.076659036, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MAX, the last average value is 150.665. The average for this metric is 150.802.", "is_anomalous": false}, {"value": 147.48, "average": 150.674615385, "min_value": 144.26516341, "max_value": 157.084067359, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "7dab1b1b5f52ab1a9cf904a49876086a", "metric_id": "f58b0205e8c69ebb8a7f2d49f6b44933", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "detected_at": "2023-01-02T10:44:21.905000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MAX", "metric_name": "average", "anomaly_score": -1.495267644, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 147.48, "min_metric_value": 144.26516341, "max_metric_value": 157.084067359, "training_avg": 150.674615385, "training_stddev": 2.136483992, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MAX, the last average value is 147.48. The average for this metric is 150.675.", "is_anomalous": false}, {"value": 149.63, "average": 150.635925926, "min_value": 144.322070232, "max_value": 156.949781619, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "14d894070390b2c86e4883bec6e0fbaa", "metric_id": "1ac683ad2c82cb505cf03fbf54c2ff41", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "detected_at": "2023-01-02T10:44:21.905000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MAX", "metric_name": "average", "anomaly_score": -0.4779611579, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 149.63, "min_metric_value": 144.322070232, "max_metric_value": 156.949781619, "training_avg": 150.635925926, "training_stddev": 2.104618564, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MAX, the last average value is 149.63. The average for this metric is 150.636.", "is_anomalous": false}, {"value": 150.65, "average": 150.636428571, "min_value": 144.440594141, "max_value": 156.832263002, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "856eec580abdb1dcc62e491d22bb9873", "metric_id": "a70b666b63ea2a62d8df5c75d62d86de", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "detected_at": "2023-01-02T10:44:21.905000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MAX", "metric_name": "average", "anomaly_score": 0.006571235267, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 150.65, "min_metric_value": 144.440594141, "max_metric_value": 156.832263002, "training_avg": 150.636428571, "training_stddev": 2.065278143, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MAX, the last average value is 150.65. The average for this metric is 150.636.", "is_anomalous": false}, {"value": 149.105, "average": 150.58362069, "min_value": 144.439908883, "max_value": 156.727332497, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "b398df791c2b523d0223b47511f54a13", "metric_id": "fc0832224270a73f6155f0572a213462", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "detected_at": "2023-01-02T10:44:21.905000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MAX", "metric_name": "average", "anomaly_score": -0.7220166258, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 149.105, "min_metric_value": 144.439908883, "max_metric_value": 156.727332497, "training_avg": 150.58362069, "training_stddev": 2.047903936, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MAX, the last average value is 149.105. The average for this metric is 150.584.", "is_anomalous": false}, {"value": 204.19, "average": 152.3705, "min_value": 144.439908883, "max_value": 156.727332497, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "7aef31f7588a7ff1856dd681167a2bf1", "metric_id": "1b8ed908758e2ae5d2065393f4e6eeb9", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "detected_at": "2023-01-02T10:44:21.905000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MAX", "metric_name": "average", "anomaly_score": 5.186167472, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 204.19, "min_metric_value": 122.394896467, "max_metric_value": 182.346103533, "training_avg": 152.3705, "training_stddev": 9.991867844, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MAX, the last average value is 204.19. The average for this metric is 152.37.", "is_anomalous": true}], "result_description": "In column MAX, the last average value is 204.19. The average for this metric is 152.37."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": "STANDARD_DEVIATION", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:25+02:00", "latest_run_time_utc": "2023-01-02T10:44:25+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_standard_deviation__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('STANDARD_DEVIATION' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column STANDARD_DEVIATION, the last null_count value is 0. The average for this metric is 0.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_standard_deviation__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('STANDARD_DEVIATION' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Null Count", "metrics": [{"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "27775ead0cc63c30bbcce64058fcc8b7", "metric_id": "ca2049cc634e9c0c99e966f2307d7d99", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "4b5736fe7df1ca54da9af3e46241be32", "metric_id": "3ecb9bc4333b88b6c3273d6d814ed9eb", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "f94b88a4f48a4750e80de72a71de9c8b", "metric_id": "181dea130e5a7e87433a180dc2cf4bf0", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "0ed7b92fefeb50bdf71f4f963945cf84", "metric_id": "f6bebba456d216a20b61824c54bcb70f", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "f73e37f9a87b491b4b0176cb7da04594", "metric_id": "5374c31af9a915455e145f043caadf4a", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "ea413ffce7b75ebf7323262cbc3f40de", "metric_id": "8d36f390a548d4b8671b529d9d547e81", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "8b1d0bbbf796e8b39524e46a2fb86691", "metric_id": "12ef3c516eb9634ad4a1956260133cf1", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "e09b4478796f78a5c36b2d0c0572123f", "metric_id": "e1660b7d117819122253ba38cf7091e0", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "9fe875bda3e8a3486c867276175ce4bf", "metric_id": "94bfc570aee1f9690db1496623a329fb", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "6f3b73dce57087000d49caed3bce1406", "metric_id": "96162ee3f8ae615b4e84ecca34cd4206", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "db26c9d55c10c3990cd05badb87015e5", "metric_id": "b8c21f02758b54ffb2daaba1800c9206", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "a9a83f573af8e18ef53ec854e66df7d7", "metric_id": "a2be25290e04bf59a469471c6b2b589d", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "2b2cf99b7464164e5ac4fbebfb0fbb9a", "metric_id": "04860fd80c440966339599a6a551d2da", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "dfda77967ced3588086181ddf169ae44", "metric_id": "b7d06656e26f0d67f1ed1e667dcd6201", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "38aab1ba504c0b8b3f28f7863c15beba", "metric_id": "8d651e44872d1787c81afaf58255ac21", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "815a950a18f76c85517e9db53600bb91", "metric_id": "fec765c5afb13263b9df5c38777a8768", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "743ef01cdac6eaf08d98c989830abb0d", "metric_id": "784b157e798a1652e7ac289c953a69dd", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "2ab3653f69a404a04a90514fc4ec9bfa", "metric_id": "6d2576af2924186a9513a83dbc08124b", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "a22b7b10f49cae00c56973d44460c680", "metric_id": "8d674de0bffc2f2aaa414200281b9a30", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "238607674ba07aa6c6625a6308400313", "metric_id": "8b3a47201e786a3c2806ae8db42cf716", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "95deed44cf70bc14c6046b37e5842b84", "metric_id": "39de05bf87d8727df4813fdb5b193034", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "da6a8d45f960fca517b2cb410ce9fecc", "metric_id": "26555e20575c4f0afdd5383435e4c597", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "c9a56ebc72704c1b80dc7f1ea56efd97", "metric_id": "14c1c42a5f607b60ed8339f7d54c5baa", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "8ad6cce015c2acc58f89b605f2938f90", "metric_id": "3572305b45dc496044a60d4964f39af8", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "891ce6b57f335fdbabea937ad4a89d21", "metric_id": "71c7a0f948a1c5b333c14fb65c9b441b", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "10537d1138fbb5d7c208ccfc46a9dfbb", "metric_id": "831629a64223b085cb642204ff9b2173", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "d664132ed66e61d4fd7607e5de40de66", "metric_id": "3f04d13ef447a4554ca35a873b5ba886", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "4d518374aba74e3cf9cf67a88b2f8072", "metric_id": "b5e7ea4fccaf65f3dc50d694d6c297dd", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "1a7e443c30698e3f608ab2e1e8282c3e", "metric_id": "24d6dd843eb016c4087a6d924a083039", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}], "result_description": "In column STANDARD_DEVIATION, the last null_count value is 0. The average for this metric is 0."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": "ZERO_PERCENT", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:24+02:00", "latest_run_time_utc": "2023-01-02T10:44:24+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_zero_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('ZERO_PERCENT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column ZERO_PERCENT, the last null_count value is 0. The average for this metric is 0.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_zero_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('ZERO_PERCENT' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Null Count", "metrics": [{"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "296bf6358ad392488ad99c45b51f2a95", "metric_id": "4c5348bb915d440a2f0bbc6824187a55", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "661a1de73ab60d41562395e1cd79f185", "metric_id": "baf595210b7411e6d6c4ab120aad39a1", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "fae2b135458058e90d9a5b6793dff633", "metric_id": "6b18eb97fc6d96591afd87f6f6fc76ad", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "b6b4eda6067648b1be063f7b51b200df", "metric_id": "048d04c8dcf4c198b28a063159bd006b", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "519c00335bc80ef7df6dda50cb02b37b", "metric_id": "19802cd09297e87e0c7d03b9e99367f1", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "afa6b0539d21142dd613ea46c39b5ce5", "metric_id": "a3e53ab2e22b4312a2bebe61e344d2ff", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "3ffcb665159515cd09a1e0710d5a10b8", "metric_id": "1089c396d336b3e0e147466a0a912be2", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "c7f28ed0755ff915ab11c130d819414f", "metric_id": "b56b8775a60c79d5ed63bf3a6b705f6e", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "430123f6c1e15299363163e731a8af08", "metric_id": "ffca88d6e6f1a893cca8a86c24925a69", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "25f56835539353494aa4c50396693200", "metric_id": "1ac92e2f434046cc4e9d5fa26f7d5188", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "9d149d9ce6d4bae543639857a3192b9e", "metric_id": "fdb6b2898fd804288d527e201d5a1d0f", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "289a8cdfb91e6019393dd0b96a44f2cd", "metric_id": "d8266a12b2c6a40c6b34f1cfb7578cb1", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "7bb876694dd0e8c60ae766f346ac67e0", "metric_id": "aa03249386de6f65300be436edeb11c6", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "722ea8c592701983e503033f293eb2a7", "metric_id": "fe82b4364d943a5a1d376cbcf3651f18", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "b105dc5286534f02d1700a90d53fc319", "metric_id": "71f3c05da897228074dc8a63e1fb8770", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "6d9938b68117e7a36ba8be9e6533e09a", "metric_id": "79a37e0579303bf9050dcd00ea6d9c2c", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "06eae80174a96f9acd60eb6e05e413ba", "metric_id": "9d062360f2258f2bdf822cbe1ce9983e", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "9ef0fa3a1c304aa8514f496300a9f9f2", "metric_id": "3a69757ed91775b34bb298f525239997", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "3e36c6fb9da5f0f36b7d605608b22297", "metric_id": "549ec83f2a765be1acc6400a026ba664", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "91539701037a21c5094f45bbbb4f967a", "metric_id": "000b0ccaf7a0de6e261bff383a1dd0bd", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "5bec1b0210f48ef18b54e3d23a363db4", "metric_id": "6ad566c9d6c382e320ef349fa5c1352e", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "feec1a95bb068f00e09fb3699d044f25", "metric_id": "58a8eb3364e60cb761e728711685eaaa", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "69d6aabaaa0c562a81897d707b6fb265", "metric_id": "0400f329aceaefa4ac26f51b0c8ab468", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "57d9974ca1669e0acb64312de6287d39", "metric_id": "7124a9f1e47850661e1f427ac798a791", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "9262ef7fc9e916dc5efc4093062e53ba", "metric_id": "2b50ef164f85e25bd10bad80cbd7ca13", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "9783dcf1e74f9364d4f51c3a25efc64f", "metric_id": "d83224877664dc00357b712fed0aee3b", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "db823fe438e82acc37703763b202d649", "metric_id": "c49348a1aee8a2567c00804b5a983b89", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "4a3cf8974322763f5c65795d08b1a40e", "metric_id": "d53901e569ba0ac52dae03e6caf44d56", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "97f587d105f4e0937c9c5ec1c271dbed", "metric_id": "a8b0dc987ec6aaa8ae5b09653c425ca0", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}], "result_description": "In column ZERO_PERCENT, the last null_count value is 0. The average for this metric is 0."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": "STANDARD_DEVIATION", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:25+02:00", "latest_run_time_utc": "2023-01-02T10:44:25+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "standard_deviation", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_standard_deviation__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('standard_deviation' as varchar)\n and upper(column_name) = upper(cast('STANDARD_DEVIATION' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column STANDARD_DEVIATION, the last standard_deviation value is 11.559. The average for this metric is 1.174.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_standard_deviation__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('standard_deviation' as varchar)\n and upper(column_name) = upper(cast('STANDARD_DEVIATION' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Standard Deviation", "metrics": [{"value": 0.8278900954, "average": 0.8225137591, "min_value": 0.7997038961, "max_value": 0.8453236221, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "1c20118ffdac26b0e45a8904ae5ff95a", "metric_id": "b8a114bfd4998da0e984ae27c3e51bf4", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "standard_deviation", "anomaly_score": 0.7071067812, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 0.8278900954, "min_metric_value": 0.7997038961, "max_metric_value": 0.8453236221, "training_avg": 0.8225137591, "training_stddev": 0.007603287664, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last standard_deviation value is 0.828. The average for this metric is 0.823.", "is_anomalous": false}, {"value": 0.8131457619, "average": 0.8193910934, "min_value": 0.7965126529, "max_value": 0.8422695339, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "b9dac95144d91d85eda6de082893f295", "metric_id": "dcc4e663e3fa4a42e53be8671370a5d9", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "standard_deviation", "anomaly_score": -0.8189366927, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 0.8131457619, "min_metric_value": 0.7965126529, "max_metric_value": 0.8422695339, "training_avg": 0.8193910934, "training_stddev": 0.007626146829, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last standard_deviation value is 0.813. The average for this metric is 0.819.", "is_anomalous": false}, {"value": 0.8140567807, "average": 0.8180575152, "min_value": 0.7977357978, "max_value": 0.8383792327, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "a3d761143180dade1ca55487334c2b57", "metric_id": "ae2d2965228fac01d96356309e1b7520", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "standard_deviation", "anomaly_score": -0.5906097058, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 0.8140567807, "min_metric_value": 0.7977357978, "max_metric_value": 0.8383792327, "training_avg": 0.8180575152, "training_stddev": 0.006773905811, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last standard_deviation value is 0.814. The average for this metric is 0.818.", "is_anomalous": false}, {"value": 0.7987270022, "average": 0.8141914126, "min_value": 0.7828492227, "max_value": 0.8455336025, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "d8d744afa5d6471b0e3047b54beda2fe", "metric_id": "db28743fbc57662136c0f9424698661d", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "standard_deviation", "anomaly_score": -1.480216644, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 0.7987270022, "min_metric_value": 0.7828492227, "max_metric_value": 0.8455336025, "training_avg": 0.8141914126, "training_stddev": 0.01044739663, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last standard_deviation value is 0.799. The average for this metric is 0.814.", "is_anomalous": false}, {"value": 0.8051966894, "average": 0.8126922921, "min_value": 0.782572135, "max_value": 0.8428124492, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "db832db4765286e8a5b4e3407ebb4051", "metric_id": "fdd651a84cf5441f086354885c477da7", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "standard_deviation", "anomaly_score": -0.7465700831, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 0.8051966894, "min_metric_value": 0.782572135, "max_metric_value": 0.8428124492, "training_avg": 0.8126922921, "training_stddev": 0.01004005237, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last standard_deviation value is 0.805. The average for this metric is 0.813.", "is_anomalous": false}, {"value": 0.7969634582, "average": 0.8104453158, "min_value": 0.7776718398, "max_value": 0.8432187919, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "c36984fc3df4b95f4920b293a07e6d26", "metric_id": "a00292657e969584f9a81270a2747658", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "standard_deviation", "anomaly_score": -1.234094689, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 0.7969634582, "min_metric_value": 0.7776718398, "max_metric_value": 0.8432187919, "training_avg": 0.8104453158, "training_stddev": 0.01092449202, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last standard_deviation value is 0.797. The average for this metric is 0.81.", "is_anomalous": false}, {"value": 0.8122182542, "average": 0.8106669331, "min_value": 0.780266374, "max_value": 0.8410674922, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "cc01325da8784e69696305d5bb463bf4", "metric_id": "1e8d0496c978fbcf3a7e73aab83cd8ff", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "standard_deviation", "anomaly_score": 0.1530880811, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 0.8122182542, "min_metric_value": 0.780266374, "max_metric_value": 0.8410674922, "training_avg": 0.8106669331, "training_stddev": 0.01013351971, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last standard_deviation value is 0.812. The average for this metric is 0.811.", "is_anomalous": false}, {"value": 0.7663625498, "average": 0.8057442239, "min_value": 0.753098729, "max_value": 0.8583897187, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "f028455846c986e4dd2dd9f5d4811021", "metric_id": "e4daa8b1ac52bd8fd509d50e2c9d4067", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "standard_deviation", "anomaly_score": -2.24416206, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 0.7663625498, "min_metric_value": 0.753098729, "max_metric_value": 0.8583897187, "training_avg": 0.8057442239, "training_stddev": 0.01754849827, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last standard_deviation value is 0.766. The average for this metric is 0.806.", "is_anomalous": false}, {"value": 0.8254129468, "average": 0.8077110962, "min_value": 0.7546849599, "max_value": 0.8607372324, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "a5e27c542976b6d11e845cd758ca35bb", "metric_id": "6e9cbedce77dabd4f3646ebc4c33b62e", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "standard_deviation", "anomaly_score": 1.001497668, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 0.8254129468, "min_metric_value": 0.7546849599, "max_metric_value": 0.8607372324, "training_avg": 0.8077110962, "training_stddev": 0.01767537875, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last standard_deviation value is 0.825. The average for this metric is 0.808.", "is_anomalous": false}, {"value": 0.808000199, "average": 0.8077373782, "min_value": 0.7574316887, "max_value": 0.8580430677, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "6bb7eb35c8b29c2c7b8779f9ba0a5d26", "metric_id": "013c3469759d1ebc224fde52ea53cf9d", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "standard_deviation", "anomaly_score": 0.01567342304, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 0.808000199, "min_metric_value": 0.7574316887, "max_metric_value": 0.8580430677, "training_avg": 0.8077373782, "training_stddev": 0.01676856317, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last standard_deviation value is 0.808. The average for this metric is 0.808.", "is_anomalous": false}, {"value": 0.8138870071, "average": 0.8082498473, "min_value": 0.7599904893, "max_value": 0.8565092053, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "f2f9f4ffedf6f118db38587bc3c8aacb", "metric_id": "bb2c0b1785ee086bfd29e3d9a8ba724b", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "standard_deviation", "anomaly_score": 0.3504290165, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 0.8138870071, "min_metric_value": 0.7599904893, "max_metric_value": 0.8565092053, "training_avg": 0.8082498473, "training_stddev": 0.01608645266, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last standard_deviation value is 0.814. The average for this metric is 0.808.", "is_anomalous": false}, {"value": 0.8060075938, "average": 0.8080773663, "min_value": 0.7618348979, "max_value": 0.8543198346, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "76e6ed79c0da7ae3d9fef486effd873a", "metric_id": "353488888dab344ef8a2def0ea584afd", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "standard_deviation", "anomaly_score": -0.1342773799, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 0.8060075938, "min_metric_value": 0.7618348979, "max_metric_value": 0.8543198346, "training_avg": 0.8080773663, "training_stddev": 0.01541415611, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last standard_deviation value is 0.806. The average for this metric is 0.808.", "is_anomalous": false}, {"value": 0.822897809, "average": 0.8091359693, "min_value": 0.7631460042, "max_value": 0.8551259344, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "854292db4db159f422ac6b35dd7b636a", "metric_id": "7447cae0d08a0acd4ef07765c2aa3cbb", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "standard_deviation", "anomaly_score": 0.8977071178, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 0.822897809, "min_metric_value": 0.7631460042, "max_metric_value": 0.8551259344, "training_avg": 0.8091359693, "training_stddev": 0.01532998836, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last standard_deviation value is 0.823. The average for this metric is 0.809.", "is_anomalous": false}, {"value": 0.8183817836, "average": 0.8097523569, "min_value": 0.7648603641, "max_value": 0.8546443497, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "188e62c78eb7bfa35612bda83cf2a910", "metric_id": "f677fc08de04b7a9af0c6caafe6a47dd", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "standard_deviation", "anomaly_score": 0.5766792335, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 0.8183817836, "min_metric_value": 0.7648603641, "max_metric_value": 0.8546443497, "training_avg": 0.8097523569, "training_stddev": 0.0149639976, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last standard_deviation value is 0.818. The average for this metric is 0.81.", "is_anomalous": false}, {"value": 0.841376594, "average": 0.8117288717, "min_value": 0.7622971993, "max_value": 0.8611605442, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "963b47dbe3c8d69d7b3cb8a15207ca2d", "metric_id": "6e42013ae346b20e625b2f6a1b9fada3", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "standard_deviation", "anomaly_score": 1.799315343, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 0.841376594, "min_metric_value": 0.7622971993, "max_metric_value": 0.8611605442, "training_avg": 0.8117288717, "training_stddev": 0.01647722414, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last standard_deviation value is 0.841. The average for this metric is 0.812.", "is_anomalous": false}, {"value": 0.7954645304, "average": 0.8107721458, "min_value": 0.7614688319, "max_value": 0.8600754596, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "b1636e4b9b0826e3ee3f73a080860795", "metric_id": "d543a930ac5d4d399b7fe0db4f405668", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "standard_deviation", "anomaly_score": -0.9314352851, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 0.7954645304, "min_metric_value": 0.7614688319, "max_metric_value": 0.8600754596, "training_avg": 0.8107721458, "training_stddev": 0.01643443795, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last standard_deviation value is 0.795. The average for this metric is 0.811.", "is_anomalous": false}, {"value": 0.7860434604, "average": 0.8093983299, "min_value": 0.7584711125, "max_value": 0.8603255474, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "4747ad48ab24cb9ba7a1c8bbead95845", "metric_id": "fef8dddc1575e3395ccfd09e7547ac33", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "standard_deviation", "anomaly_score": -1.375779242, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 0.7860434604, "min_metric_value": 0.7584711125, "max_metric_value": 0.8603255474, "training_avg": 0.8093983299, "training_stddev": 0.01697573915, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last standard_deviation value is 0.786. The average for this metric is 0.809.", "is_anomalous": false}, {"value": 0.8357435831, "average": 0.8107849222, "min_value": 0.758075675, "max_value": 0.8634941694, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "b9388ce589abec26c65acbe193bd0886", "metric_id": "fb7f2693b9aad689a24643ee523780f8", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "standard_deviation", "anomaly_score": 1.420547375, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 0.8357435831, "min_metric_value": 0.758075675, "max_metric_value": 0.8634941694, "training_avg": 0.8107849222, "training_stddev": 0.01756974906, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last standard_deviation value is 0.836. The average for this metric is 0.811.", "is_anomalous": false}, {"value": 0.8277686905, "average": 0.8116341106, "min_value": 0.75908088, "max_value": 0.8641873413, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "462cd299481c7e809034d67d5b1959b2", "metric_id": "10d8ef821777c73470d02b101a1cbcba", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "standard_deviation", "anomaly_score": 0.9210421348, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 0.8277686905, "min_metric_value": 0.75908088, "max_metric_value": 0.8641873413, "training_avg": 0.8116341106, "training_stddev": 0.01751774355, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last standard_deviation value is 0.828. The average for this metric is 0.812.", "is_anomalous": false}, {"value": 0.8423316527, "average": 0.8130958983, "min_value": 0.7580721802, "max_value": 0.8681196165, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "4eaab356592f9ad4d646829bb0f8ee4f", "metric_id": "50cd93a030bc90cd46976c27b06273ab", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "standard_deviation", "anomaly_score": 1.593990118, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 0.8423316527, "min_metric_value": 0.7580721802, "max_metric_value": 0.8681196165, "training_avg": 0.8130958983, "training_stddev": 0.01834123938, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last standard_deviation value is 0.842. The average for this metric is 0.813.", "is_anomalous": false}, {"value": 0.8100964643, "average": 0.8129595604, "min_value": 0.7592276509, "max_value": 0.86669147, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "52aab3573b8a9d3b0fc37f065a46540b", "metric_id": "be28eaecb72ff1bb82497487aca30e56", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "standard_deviation", "anomaly_score": -0.1598545175, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 0.8100964643, "min_metric_value": 0.7592276509, "max_metric_value": 0.86669147, "training_avg": 0.8129595604, "training_stddev": 0.01791063652, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last standard_deviation value is 0.81. The average for this metric is 0.813.", "is_anomalous": false}, {"value": 0.810716539, "average": 0.8128620377, "min_value": 0.7603467621, "max_value": 0.8653773133, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "a7bc26b78cd65ea2d0d07757c0672125", "metric_id": "a5181f6b49ebac42a97e96bbae8b4207", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "standard_deviation", "anomaly_score": -0.1225642693, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 0.810716539, "min_metric_value": 0.7603467621, "max_metric_value": 0.8653773133, "training_avg": 0.8128620377, "training_stddev": 0.01750509187, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last standard_deviation value is 0.811. The average for this metric is 0.813.", "is_anomalous": false}, {"value": 0.8372604285, "average": 0.8138786374, "min_value": 0.7603886568, "max_value": 0.8673686179, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "fe08e1e9f167aec57a5d696619b8d627", "metric_id": "063fde690e242da67f37df1ec06b8b85", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "standard_deviation", "anomaly_score": 1.311374068, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 0.8372604285, "min_metric_value": 0.7603886568, "max_metric_value": 0.8673686179, "training_avg": 0.8138786374, "training_stddev": 0.01782999352, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last standard_deviation value is 0.837. The average for this metric is 0.814.", "is_anomalous": false}, {"value": 0.8017568649, "average": 0.8133937665, "min_value": 0.7605273336, "max_value": 0.8662601993, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "c3330238f5b1164e93bfb145d3f91c93", "metric_id": "6954b79efba40575f5867d5f6fc7de62", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "standard_deviation", "anomaly_score": -0.6603567315, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 0.8017568649, "min_metric_value": 0.7605273336, "max_metric_value": 0.8662601993, "training_avg": 0.8133937665, "training_stddev": 0.01762214428, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last standard_deviation value is 0.802. The average for this metric is 0.813.", "is_anomalous": false}, {"value": 0.8317807983, "average": 0.81410096, "min_value": 0.7611850452, "max_value": 0.8670168748, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "3aa0d151464f44c7b6b00a597bf18897", "metric_id": "51f8aeb3a03e1248d32b8c486db561a6", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "standard_deviation", "anomaly_score": 1.002335785, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 0.8317807983, "min_metric_value": 0.7611850452, "max_metric_value": 0.8670168748, "training_avg": 0.81410096, "training_stddev": 0.01763863828, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last standard_deviation value is 0.832. The average for this metric is 0.814.", "is_anomalous": false}, {"value": 0.8372604285, "average": 0.8149587181, "min_value": 0.7613752774, "max_value": 0.8685421587, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "0756d3851168b648b6b5eeb8dc136159", "metric_id": "bacc95d06abe26117fa0c34f82a30b51", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "standard_deviation", "anomaly_score": 1.248615811, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 0.8372604285, "min_metric_value": 0.7613752774, "max_metric_value": 0.8685421587, "training_avg": 0.8149587181, "training_stddev": 0.01786114689, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last standard_deviation value is 0.837. The average for this metric is 0.815.", "is_anomalous": false}, {"value": 0.8365999626, "average": 0.8157316197, "min_value": 0.7617373247, "max_value": 0.8697259147, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "7d572ea59b1e8e0dadd00f84fb98686c", "metric_id": "bd57fa9597ba4142d945e78c12a97a4a", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "standard_deviation", "anomaly_score": 1.159474883, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 0.8365999626, "min_metric_value": 0.7617373247, "max_metric_value": 0.8697259147, "training_avg": 0.8157316197, "training_stddev": 0.01799809834, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last standard_deviation value is 0.837. The average for this metric is 0.816.", "is_anomalous": false}, {"value": 0.8081867539, "average": 0.8154714519, "min_value": 0.7622837708, "max_value": 0.868659133, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "0a2f3be35dc4396f4590d5f65a2640f4", "metric_id": "aeb5f3638d3d80f0eb082c637412d6cb", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "standard_deviation", "anomaly_score": -0.4108863855, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 0.8081867539, "min_metric_value": 0.7622837708, "max_metric_value": 0.868659133, "training_avg": 0.8154714519, "training_stddev": 0.01772922703, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last standard_deviation value is 0.808. The average for this metric is 0.815.", "is_anomalous": false}, {"value": 11.55876291, "average": 1.173581167, "min_value": 0.7622837708, "max_value": 0.868659133, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "895fa6470ecb0cd7576ced4675a8a1e0", "metric_id": "b644e729e283c33d7fd173e8555e9948", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "standard_deviation", "anomaly_score": 5.294442571, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 11.55876291, "min_metric_value": -4.71099399, "max_metric_value": 7.058156325, "training_avg": 1.173581167, "training_stddev": 1.961525052, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last standard_deviation value is 11.559. The average for this metric is 1.174.", "is_anomalous": true}], "result_description": "In column STANDARD_DEVIATION, the last standard_deviation value is 11.559. The average for this metric is 1.174."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": "ZERO_PERCENT", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:24+02:00", "latest_run_time_utc": "2023-01-02T10:44:24+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "variance", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_zero_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('variance' as varchar)\n and upper(column_name) = upper(cast('ZERO_PERCENT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column ZERO_PERCENT, the last variance value is 5856.389. The average for this metric is 4295.229.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_zero_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('variance' as varchar)\n and upper(column_name) = upper(cast('ZERO_PERCENT' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Variance", "metrics": [{"value": 4678.064698492, "average": 4628.74968593, "min_value": 4419.523807145, "max_value": 4837.975564715, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "139098f0908d4837443c70eeee7ebaed", "metric_id": "42bc0d1e6b211645ec5e9d4bfabccbea", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "variance", "anomaly_score": 0.7071067812, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 4678.064698492, "min_metric_value": 4419.523807145, "max_metric_value": 4837.975564715, "training_avg": 4628.74968593, "training_stddev": 69.741959595, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last variance value is 4678.065. The average for this metric is 4628.75.", "is_anomalous": false}, {"value": 4203.08419598, "average": 4486.86118928, "min_value": 3734.889738905, "max_value": 5238.832639655, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "d9ea2976057e246fffa9e30cfc38c400", "metric_id": "2354c609652d6eda1c5b6c21d9eab4c6", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "variance", "anomaly_score": -1.13213205, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 4203.08419598, "min_metric_value": 3734.889738905, "max_metric_value": 5238.832639655, "training_avg": 4486.86118928, "training_stddev": 250.657150125, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last variance value is 4203.084. The average for this metric is 4486.861.", "is_anomalous": false}, {"value": 4521.394949749, "average": 4495.494629397, "min_value": 3879.331219391, "max_value": 5111.658039403, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "9562d11509159887f18c1735ff1240cd", "metric_id": "f64ed56afe6db35a192cffac08d1248b", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "variance", "anomaly_score": 0.1261044713, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 4521.394949749, "min_metric_value": 3879.331219391, "max_metric_value": 5111.658039403, "training_avg": 4495.494629397, "training_stddev": 205.387803335, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last variance value is 4521.395. The average for this metric is 4495.495.", "is_anomalous": false}, {"value": 4444.683316583, "average": 4485.332366834, "min_value": 3947.382339424, "max_value": 5023.282394244, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "e488d17218d2c028dcf3671ce95bca32", "metric_id": "dcca2a07d5c86b29537ba042240e2b51", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "variance", "anomaly_score": -0.2266886226, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 4444.683316583, "min_metric_value": 3947.382339424, "max_metric_value": 5023.282394244, "training_avg": 4485.332366834, "training_stddev": 179.316675803, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last variance value is 4444.683. The average for this metric is 4485.332.", "is_anomalous": false}, {"value": 4233.149522613, "average": 4443.301892797, "min_value": 3871.544543941, "max_value": 5015.059241654, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "f6f4b606fd40189fa3efc759acabaccf", "metric_id": "e6a734b197d4a7cf76a0830a6d4b3193", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "variance", "anomaly_score": -1.102665513, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 4233.149522613, "min_metric_value": 3871.544543941, "max_metric_value": 5015.059241654, "training_avg": 4443.301892797, "training_stddev": 190.585782952, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last variance value is 4233.15. The average for this metric is 4443.302.", "is_anomalous": false}, {"value": 4437.52201005, "average": 4442.476195262, "min_value": 3920.494388213, "max_value": 4964.458002311, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "e6bba929268c7ebbc37a6c7442326c0b", "metric_id": "a2bac823e126bc57ca9edbf0070b187f", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "variance", "anomaly_score": -0.02847332117, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 4437.52201005, "min_metric_value": 3920.494388213, "max_metric_value": 4964.458002311, "training_avg": 4442.476195262, "training_stddev": 173.993935683, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last variance value is 4437.522. The average for this metric is 4442.476.", "is_anomalous": false}, {"value": 4169.929648241, "average": 4408.407876884, "min_value": 3845.284216125, "max_value": 4971.531537644, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "976b51b82a4ff719dd5e47de35594b73", "metric_id": "e9fff42e5dc15ab4ad4e4224520f91e2", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "variance", "anomaly_score": -1.270475272, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 4169.929648241, "min_metric_value": 3845.284216125, "max_metric_value": 4971.531537644, "training_avg": 4408.407876884, "training_stddev": 187.70788692, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last variance value is 4169.93. The average for this metric is 4408.408.", "is_anomalous": false}, {"value": 4339.133165829, "average": 4400.710686767, "min_value": 3869.421019321, "max_value": 4932.000354214, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "3ea600123588679d2b981a8e927a702b", "metric_id": "eb692226e476969a309aff6b292d743b", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "variance", "anomaly_score": -0.3477059204, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 4339.133165829, "min_metric_value": 3869.421019321, "max_metric_value": 4932.000354214, "training_avg": 4400.710686767, "training_stddev": 177.096555816, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last variance value is 4339.133. The average for this metric is 4400.711.", "is_anomalous": false}, {"value": 4441.195979899, "average": 4404.75921608, "min_value": 3902.384183144, "max_value": 4907.134249017, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "00513863a32cfa5ddea12d9d7ea9e57e", "metric_id": "e8b7cc1387ba714ac116967c0f00a439", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "variance", "anomaly_score": 0.2175870302, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 4441.195979899, "min_metric_value": 3902.384183144, "max_metric_value": 4907.134249017, "training_avg": 4404.75921608, "training_stddev": 167.458344312, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last variance value is 4441.196. The average for this metric is 4404.759.", "is_anomalous": false}, {"value": 4417.553366834, "average": 4405.922320694, "min_value": 3929.187032754, "max_value": 4882.657608635, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "96cd3d16d1d78715441fd2d97cc4104e", "metric_id": "fd68497213ae5e79f71b0db0675d682e", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "variance", "anomaly_score": 0.07319185154, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 4417.553366834, "min_metric_value": 3929.187032754, "max_metric_value": 4882.657608635, "training_avg": 4405.922320694, "training_stddev": 158.911762647, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last variance value is 4417.553. The average for this metric is 4405.922.", "is_anomalous": false}, {"value": 4541.68620603, "average": 4417.235977806, "min_value": 3947.726759549, "max_value": 4886.745196063, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "2d6d5608fdfe6abb8144260e1c54ad6d", "metric_id": "faffa4cff3459649082207112b94e05a", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "variance", "anomaly_score": 0.795193513, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 4541.68620603, "min_metric_value": 3947.726759549, "max_metric_value": 4886.745196063, "training_avg": 4417.235977806, "training_stddev": 156.503072752, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last variance value is 4541.686. The average for this metric is 4417.236.", "is_anomalous": false}, {"value": 4325.786834171, "average": 4410.201428295, "min_value": 3954.286163197, "max_value": 4866.116693394, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "cd46d3c0282a9cc2c8a5b2bd5c21c282", "metric_id": "776fbcd788c924147cf079e0ef7311e5", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "variance", "anomaly_score": -0.5554623891, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 4325.786834171, "min_metric_value": 3954.286163197, "max_metric_value": 4866.116693394, "training_avg": 4410.201428295, "training_stddev": 151.971755033, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last variance value is 4325.787. The average for this metric is 4410.201.", "is_anomalous": false}, {"value": 4449.259396985, "average": 4412.991283202, "min_value": 3973.844052146, "max_value": 4852.138514258, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "2e05d4e91c5b515f87972bf3a44d7536", "metric_id": "151a709cafd4dbc0178481aac2d22fd9", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "variance", "anomaly_score": 0.2477627858, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 4449.259396985, "min_metric_value": 3973.844052146, "max_metric_value": 4852.138514258, "training_avg": 4412.991283202, "training_stddev": 146.382410352, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last variance value is 4449.259. The average for this metric is 4412.991.", "is_anomalous": false}, {"value": 4281.451231156, "average": 4404.221946399, "min_value": 3968.955429232, "max_value": 4839.488463565, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "fa73a8a83f2584a40ae153d753f3a8c2", "metric_id": "ad998c95fd92fb28f5ecd762da4bc6b9", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "variance", "anomaly_score": -0.84617615, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 4281.451231156, "min_metric_value": 3968.955429232, "max_metric_value": 4839.488463565, "training_avg": 4404.221946399, "training_stddev": 145.088839056, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last variance value is 4281.451. The average for this metric is 4404.222.", "is_anomalous": false}, {"value": 4506.994974874, "average": 4410.645260678, "min_value": 3983.131787923, "max_value": 4838.158733434, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "46ab695a15077c23b9786d648b06055d", "metric_id": "4aa11356c8475a987504561de5a0862f", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "variance", "anomaly_score": 0.6761170373, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 4506.994974874, "min_metric_value": 3983.131787923, "max_metric_value": 4838.158733434, "training_avg": 4410.645260678, "training_stddev": 142.504490919, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last variance value is 4506.995. The average for this metric is 4410.645.", "is_anomalous": false}, {"value": 3879.416859296, "average": 4379.396531185, "min_value": 3813.051497372, "max_value": 4945.741564999, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "67b2ecf8c1b788bc40d6d0ef092c82d4", "metric_id": "52c58828d96af41c7b74046fc780a530", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "variance", "anomaly_score": -2.648454433, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 3879.416859296, "min_metric_value": 3813.051497372, "max_metric_value": 4945.741564999, "training_avg": 4379.396531185, "training_stddev": 188.781677938, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last variance value is 3879.417. The average for this metric is 4379.397.", "is_anomalous": false}, {"value": 4209.355552764, "average": 4369.949810162, "min_value": 3807.512069863, "max_value": 4932.387550461, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "2fee845805c510071f3b6c07fcb98998", "metric_id": "85c8f47103a841e507d7e6db323c51df", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "variance", "anomaly_score": -0.8565975177, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 4209.355552764, "min_metric_value": 3807.512069863, "max_metric_value": 4932.387550461, "training_avg": 4369.949810162, "training_stddev": 187.479246766, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last variance value is 4209.356. The average for this metric is 4369.95.", "is_anomalous": false}, {"value": 3603.798366834, "average": 4329.626049987, "min_value": 3570.146661685, "max_value": 5089.105438289, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "fb34e6435c6716c31149c7d61478e63b", "metric_id": "2cdca76e99d4314d55b938500e8a7a81", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "variance", "anomaly_score": -2.867073265, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 3603.798366834, "min_metric_value": 3570.146661685, "max_metric_value": 5089.105438289, "training_avg": 4329.626049987, "training_stddev": 253.159796101, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last variance value is 3603.798. The average for this metric is 4329.626.", "is_anomalous": false}, {"value": 4119.886934673, "average": 4319.139094221, "min_value": 3566.645705979, "max_value": 5071.632482463, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "1e677393042e3327b7124de47af0a1ff", "metric_id": "78746280687f37e2e9e308eb97f8c310", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "variance", "anomaly_score": -0.7943677486, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 4119.886934673, "min_metric_value": 3566.645705979, "max_metric_value": 5071.632482463, "training_avg": 4319.139094221, "training_stddev": 250.831129414, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last variance value is 4119.887. The average for this metric is 4319.139.", "is_anomalous": false}, {"value": 3398.776984925, "average": 4275.312327112, "min_value": 3326.121910618, "max_value": 5224.502743606, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "43f9996c05c87b4c7abaff04093ef61d", "metric_id": "8f0afe4353a56717fc2f254e9b2a4495", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "variance", "anomaly_score": -2.770367232, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 3398.776984925, "min_metric_value": 3326.121910618, "max_metric_value": 5224.502743606, "training_avg": 4275.312327112, "training_stddev": 316.396805498, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last variance value is 3398.777. The average for this metric is 4275.312.", "is_anomalous": false}, {"value": 4308.141809045, "average": 4276.804576291, "min_value": 3350.251619889, "max_value": 5203.357532692, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "d6ff13c983aeab05655926583d3fcce9", "metric_id": "809860a4d343de5f602bd47799c51811", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "variance", "anomaly_score": 0.1014639235, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 4308.141809045, "min_metric_value": 3350.251619889, "max_metric_value": 5203.357532692, "training_avg": 4276.804576291, "training_stddev": 308.850985467, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last variance value is 4308.142. The average for this metric is 4276.805.", "is_anomalous": false}, {"value": 3718.133768844, "average": 4252.514541184, "min_value": 3282.149229481, "max_value": 5222.879852887, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "30a2c8c26a510299fc9a2aef23aacee7", "metric_id": "dd59d5a710c9dc6ed11cadcc3ed0bb09", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "variance", "anomaly_score": -1.65210184, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 3718.133768844, "min_metric_value": 3282.149229481, "max_metric_value": 5222.879852887, "training_avg": 4252.514541184, "training_stddev": 323.455103901, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last variance value is 3718.134. The average for this metric is 4252.515.", "is_anomalous": false}, {"value": 3999.953542714, "average": 4241.991166248, "min_value": 3280.435411138, "max_value": 5203.546921358, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "e160d3c8a825883bb4243734c4be6f10", "metric_id": "79615cef3ccf685d8da4936b90c47a96", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "variance", "anomaly_score": -0.7551438039, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 3999.953542714, "min_metric_value": 3280.435411138, "max_metric_value": 5203.546921358, "training_avg": 4241.991166248, "training_stddev": 320.518585037, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last variance value is 3999.954. The average for this metric is 4241.991.", "is_anomalous": false}, {"value": 4150.33919598, "average": 4238.325087437, "min_value": 3295.409959209, "max_value": 5181.240215666, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "3b8806d078199ea791f905b209232823", "metric_id": "6c19b63085d8a2a0905bee186829fb28", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "variance", "anomaly_score": -0.2799378931, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 4150.33919598, "min_metric_value": 3295.409959209, "max_metric_value": 5181.240215666, "training_avg": 4238.325087437, "training_stddev": 314.305042743, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last variance value is 4150.339. The average for this metric is 4238.325.", "is_anomalous": false}, {"value": 4180.693065327, "average": 4236.108471202, "min_value": 3311.622065589, "max_value": 5160.594876815, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "6b76f4bdd5fa225c6895dc066cab62e3", "metric_id": "645c15271a97a74e79643f13253c1dc8", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "variance", "anomaly_score": -0.1798254865, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 4180.693065327, "min_metric_value": 3311.622065589, "max_metric_value": 5160.594876815, "training_avg": 4236.108471202, "training_stddev": 308.162135204, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last variance value is 4180.693. The average for this metric is 4236.108.", "is_anomalous": false}, {"value": 4854.08, "average": 4258.996305602, "min_value": 3284.77904825, "max_value": 5233.213562954, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "bd5c216503fb54b357946dd88fc323ba", "metric_id": "7d3a01bd15497c0b858d191683dc89c3", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "variance", "anomaly_score": 1.832497905, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 4854.08, "min_metric_value": 3284.77904825, "max_metric_value": 5233.213562954, "training_avg": 4258.996305602, "training_stddev": 324.739085784, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last variance value is 4854.08. The average for this metric is 4258.996.", "is_anomalous": false}, {"value": 3608.820879397, "average": 4235.775754666, "min_value": 3211.166402608, "max_value": 5260.385106724, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "48e9b5548f51c1ac6ff6331122a1db97", "metric_id": "66662f765f16fee8522c882e6d54602f", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "variance", "anomaly_score": -1.835689497, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 3608.820879397, "min_metric_value": 3211.166402608, "max_metric_value": 5260.385106724, "training_avg": 4235.775754666, "training_stddev": 341.536450686, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last variance value is 3608.821. The average for this metric is 4235.776.", "is_anomalous": false}, {"value": 4398.770251256, "average": 4241.396254549, "min_value": 3231.160839709, "max_value": 5251.631669388, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "5170ed7621adeb274567160f97d76dd4", "metric_id": "4a899a63bf8d7e895c4fb1c02e6d742d", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "variance", "anomaly_score": 0.4673385858, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 4398.770251256, "min_metric_value": 3231.160839709, "max_metric_value": 5251.631669388, "training_avg": 4241.396254549, "training_stddev": 336.74513828, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last variance value is 4398.77. The average for this metric is 4241.396.", "is_anomalous": false}, {"value": 5856.388718593, "average": 4295.229336683, "min_value": 3231.160839709, "max_value": 5251.631669388, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "97ebbb15de297c7a1d931a8bd4afc909", "metric_id": "890074ef1481114f5ec97d8fd8a82847", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "variance", "anomaly_score": 3.522464188, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 5856.388718593, "min_metric_value": 2965.626565465, "max_metric_value": 5624.832107901, "training_avg": 4295.229336683, "training_stddev": 443.200923739, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last variance value is 5856.389. The average for this metric is 4295.229.", "is_anomalous": true}], "result_description": "In column ZERO_PERCENT, the last variance value is 5856.389. The average for this metric is 4295.229."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": "STANDARD_DEVIATION", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:25+02:00", "latest_run_time_utc": "2023-01-02T10:44:25+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "variance", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_standard_deviation__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('variance' as varchar)\n and upper(column_name) = upper(cast('STANDARD_DEVIATION' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column STANDARD_DEVIATION, the last variance value is 133.605. The average for this metric is 5.097.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_standard_deviation__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('variance' as varchar)\n and upper(column_name) = upper(cast('STANDARD_DEVIATION' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Variance", "metrics": [{"value": 0.6854020101, "average": 0.6765577889, "min_value": 0.6390349366, "max_value": 0.7140806413, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "d99cdd581e44c5d0089f289a2bd1bd09", "metric_id": "6909a57a820e290143f24aaa81a303b1", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "variance", "anomaly_score": 0.7071067812, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 0.6854020101, "min_metric_value": 0.6390349366, "max_metric_value": 0.7140806413, "training_avg": 0.6765577889, "training_stddev": 0.01250761744, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last variance value is 0.685. The average for this metric is 0.677.", "is_anomalous": false}, {"value": 0.6612060302, "average": 0.671440536, "min_value": 0.6338771001, "max_value": 0.7090039719, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "c9da30d1c1777cef09b0fcf0e1519c44", "metric_id": "75b25350b598f4e9ad45e5b5e8715d31", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "variance", "anomaly_score": -0.8173777731, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 0.6612060302, "min_metric_value": 0.6338771001, "max_metric_value": 0.7090039719, "training_avg": 0.671440536, "training_stddev": 0.0125211453, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last variance value is 0.661. The average for this metric is 0.671.", "is_anomalous": false}, {"value": 0.6626884422, "average": 0.6692525126, "min_value": 0.6358905199, "max_value": 0.7026145053, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "dc8f14322c089c859b052346c5403df3", "metric_id": "61c9de3790fcc96433bc4ba530edfcd6", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "variance", "anomaly_score": -0.5902588385, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 0.6626884422, "min_metric_value": 0.6358905199, "max_metric_value": 0.7026145053, "training_avg": 0.6692525126, "training_stddev": 0.01112066423, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last variance value is 0.663. The average for this metric is 0.669.", "is_anomalous": false}, {"value": 0.6379648241, "average": 0.6629949749, "min_value": 0.6120359529, "max_value": 0.7139539969, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "0d5ae0eae16986ce1a5bb14ea775b8c5", "metric_id": "c89e366adcdefc3b70869eeb79c68593", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "variance", "anomaly_score": -1.473545789, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 0.6379648241, "min_metric_value": 0.6120359529, "max_metric_value": 0.7139539969, "training_avg": 0.6629949749, "training_stddev": 0.01698634067, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last variance value is 0.638. The average for this metric is 0.663.", "is_anomalous": false}, {"value": 0.6483417085, "average": 0.6605527638, "min_value": 0.6115677165, "max_value": 0.7095378111, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "a8f179a6d5159bfd4e74dd9af9a5a57d", "metric_id": "faa05d2b271e46ee008e533208f56e9f", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "variance", "anomaly_score": -0.7478438389, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 0.6483417085, "min_metric_value": 0.6115677165, "max_metric_value": 0.7095378111, "training_avg": 0.6605527638, "training_stddev": 0.0163283491, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last variance value is 0.648. The average for this metric is 0.661.", "is_anomalous": false}, {"value": 0.6351507538, "average": 0.6569239052, "min_value": 0.6037333506, "max_value": 0.7101144599, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "0633f6d6166e65fc0fa0271b0d0c5be7", "metric_id": "b842515cf01c4edc0b6561e3c67a6c0b", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "variance", "anomaly_score": -1.228027323, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 0.6351507538, "min_metric_value": 0.6037333506, "max_metric_value": 0.7101144599, "training_avg": 0.6569239052, "training_stddev": 0.01773018488, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last variance value is 0.635. The average for this metric is 0.657.", "is_anomalous": false}, {"value": 0.6596984925, "average": 0.6572707286, "min_value": 0.6079379881, "max_value": 0.7066034691, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "24d0787042763bd56f48fccda9db1583", "metric_id": "f6d0e0efbfc510e06341d430ac84bfe0", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "variance", "anomaly_score": 0.1476360604, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 0.6596984925, "min_metric_value": 0.6079379881, "max_metric_value": 0.7066034691, "training_avg": 0.6572707286, "training_stddev": 0.01644424683, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last variance value is 0.66. The average for this metric is 0.657.", "is_anomalous": false}, {"value": 0.5873115578, "average": 0.6494974874, "min_value": 0.5656894332, "max_value": 0.7333055417, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "eb9346169cf53130b5379f585b88678c", "metric_id": "d3e4c460f09071303b9d56e94e598db2", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "variance", "anomaly_score": -2.22601265, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 0.5873115578, "min_metric_value": 0.5656894332, "max_metric_value": 0.7333055417, "training_avg": 0.6494974874, "training_stddev": 0.02793601809, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last variance value is 0.587. The average for this metric is 0.649.", "is_anomalous": false}, {"value": 0.6813065327, "average": 0.652678392, "min_value": 0.5680970543, "max_value": 0.7372597296, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "dae638d17de93fc99c4ca3b20071ea20", "metric_id": "f122a8e187d70425934d94892447af48", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "variance", "anomaly_score": 1.015406288, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 0.6813065327, "min_metric_value": 0.5680970543, "max_metric_value": 0.7372597296, "training_avg": 0.652678392, "training_stddev": 0.02819377921, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last variance value is 0.681. The average for this metric is 0.653.", "is_anomalous": false}, {"value": 0.6528643216, "average": 0.6526952947, "min_value": 0.5724542161, "max_value": 0.7329363732, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "1f3298d670a5d3ba9e336b4fa0add0e9", "metric_id": "7cccf0e3748a1a3b7306aa8c062c412f", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "variance", "anomaly_score": 0.006319467133, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 0.6528643216, "min_metric_value": 0.5724542161, "max_metric_value": 0.7329363732, "training_avg": 0.6526952947, "training_stddev": 0.0267470262, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last variance value is 0.653. The average for this metric is 0.653.", "is_anomalous": false}, {"value": 0.6624120603, "average": 0.6535050251, "min_value": 0.5765367701, "max_value": 0.7304732801, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "fb4b0c3ed93fd8d81b2eea0a69f758bd", "metric_id": "97bd805c7a9c029bdd7e4273a775fb76", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "variance", "anomaly_score": 0.3471704733, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 0.6624120603, "min_metric_value": 0.5765367701, "max_metric_value": 0.7304732801, "training_avg": 0.6535050251, "training_stddev": 0.02565608501, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last variance value is 0.662. The average for this metric is 0.654.", "is_anomalous": false}, {"value": 0.6496482412, "average": 0.6532083494, "min_value": 0.5794470169, "max_value": 0.726969682, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "c5f41ac0068586705da5f13a1dd47d1e", "metric_id": "23b5ce36d2d57f54686f2e769a66f888", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "variance", "anomaly_score": -0.1447957124, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 0.6496482412, "min_metric_value": 0.5794470169, "max_metric_value": 0.726969682, "training_avg": 0.6532083494, "training_stddev": 0.02458711086, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last variance value is 0.65. The average for this metric is 0.653.", "is_anomalous": false}, {"value": 0.677160804, "average": 0.6549192391, "min_value": 0.5814955629, "max_value": 0.7283429152, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "93190c30dc6a411ee7d295a9919e9553", "metric_id": "624a14580f5fe34972031ee171304d89", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "variance", "anomaly_score": 0.9087626552, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 0.677160804, "min_metric_value": 0.5814955629, "max_metric_value": 0.7283429152, "training_avg": 0.6549192391, "training_stddev": 0.02447455872, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last variance value is 0.677. The average for this metric is 0.655.", "is_anomalous": false}, {"value": 0.6697487437, "average": 0.6559078727, "min_value": 0.5842286516, "max_value": 0.7275870938, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "febf1dd467c3054740448b01244ad141", "metric_id": "bd3996b89c66aac95935591adc966080", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "variance", "anomaly_score": 0.5792838211, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 0.6697487437, "min_metric_value": 0.5842286516, "max_metric_value": 0.7275870938, "training_avg": 0.6559078727, "training_stddev": 0.02389307368, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last variance value is 0.67. The average for this metric is 0.656.", "is_anomalous": false}, {"value": 0.7079145729, "average": 0.6591582915, "min_value": 0.5796801477, "max_value": 0.7386364352, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "6f3cfaafc91ae3661411a93fdc98e0d0", "metric_id": "2ca1a252f20bf85afc515eb358af3f73", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "variance", "anomaly_score": 1.840365632, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 0.7079145729, "min_metric_value": 0.5796801477, "max_metric_value": 0.7386364352, "training_avg": 0.6591582915, "training_stddev": 0.02649271458, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last variance value is 0.708. The average for this metric is 0.659.", "is_anomalous": false}, {"value": 0.6327638191, "average": 0.6576056754, "min_value": 0.5782911043, "max_value": 0.7369202466, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "4d240a1a7d77eaeb8794345cc982ed0f", "metric_id": "6e41c37b30ee404bc4f7c1880d37fcd5", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "variance", "anomaly_score": -0.9396201469, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 0.6327638191, "min_metric_value": 0.5782911043, "max_metric_value": 0.7369202466, "training_avg": 0.6576056754, "training_stddev": 0.02643819039, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last variance value is 0.633. The average for this metric is 0.658.", "is_anomalous": false}, {"value": 0.6178643216, "average": 0.6553978224, "min_value": 0.5734805369, "max_value": 0.737315108, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "b5e2b9fc9862dead35bc42257a314a0b", "metric_id": "59b0557ca7bfcf1a6b4c3fd74b05220b", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "variance", "anomaly_score": -1.374563399, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 0.6178643216, "min_metric_value": 0.5734805369, "max_metric_value": 0.737315108, "training_avg": 0.6553978224, "training_stddev": 0.02730576185, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last variance value is 0.618. The average for this metric is 0.655.", "is_anomalous": false}, {"value": 0.6984673367, "average": 0.657664639, "min_value": 0.5727157394, "max_value": 0.7426135386, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "a1c2b5e45b5161572757006183ac4d99", "metric_id": "5a671c92cfa8c6c1741daf359625f309", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "variance", "anomaly_score": 1.440961492, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 0.6984673367, "min_metric_value": 0.5727157394, "max_metric_value": 0.7426135386, "training_avg": 0.657664639, "training_stddev": 0.02831629987, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last variance value is 0.698. The average for this metric is 0.658.", "is_anomalous": false}, {"value": 0.685201005, "average": 0.6590414573, "min_value": 0.574320016, "max_value": 0.7437628986, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "c4424f14da09d6eae872844b74ff6bea", "metric_id": "257771bd25d34f10bfdb8138924206c8", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "variance", "anomaly_score": 0.9263138352, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 0.685201005, "min_metric_value": 0.574320016, "max_metric_value": 0.7437628986, "training_avg": 0.6590414573, "training_stddev": 0.02824048044, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last variance value is 0.685. The average for this metric is 0.659.", "is_anomalous": false}, {"value": 0.7095226131, "average": 0.6614453218, "min_value": 0.5725016023, "max_value": 0.7503890413, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "55324bd68579a81f5bb46ae1c9a2bbf1", "metric_id": "94c51ceb030535cee78928cbd40a84e6", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "variance", "anomaly_score": 1.621608299, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 0.7095226131, "min_metric_value": 0.5725016023, "max_metric_value": 0.7503890413, "training_avg": 0.6614453218, "training_stddev": 0.0296479065, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last variance value is 0.71. The average for this metric is 0.661.", "is_anomalous": false}, {"value": 0.6562562814, "average": 0.6612094564, "min_value": 0.5743458455, "max_value": 0.7480730673, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "98fce0c4c9dc8669a2cf3fdc1ae68c62", "metric_id": "76f591ab5028ec36fa1d08638297522f", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "variance", "anomaly_score": -0.1710673175, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 0.6562562814, "min_metric_value": 0.5743458455, "max_metric_value": 0.7480730673, "training_avg": 0.6612094564, "training_stddev": 0.02895453696, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last variance value is 0.656. The average for this metric is 0.661.", "is_anomalous": false}, {"value": 0.6572613065, "average": 0.6610377977, "min_value": 0.5761353895, "max_value": 0.7459402059, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "947deb87ea2776bff77b3e59d7230b3c", "metric_id": "558561d886138e9ca311e408e4d674a6", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "variance", "anomaly_score": -0.1334411319, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 0.6572613065, "min_metric_value": 0.5761353895, "max_metric_value": 0.7459402059, "training_avg": 0.6610377977, "training_stddev": 0.02830080273, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last variance value is 0.657. The average for this metric is 0.661.", "is_anomalous": false}, {"value": 0.7010050251, "average": 0.6627030988, "min_value": 0.5761350526, "max_value": 0.749271145, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "ef64651a3e177149b54528cafe775ffd", "metric_id": "d11772f79a8a68c1e561a694521372fc", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "variance", "anomaly_score": 1.327346335, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 0.7010050251, "min_metric_value": 0.5761350526, "max_metric_value": 0.749271145, "training_avg": 0.6627030988, "training_stddev": 0.0288560154, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last variance value is 0.701. The average for this metric is 0.663.", "is_anomalous": false}, {"value": 0.6428140704, "average": 0.6619075377, "min_value": 0.576326103, "max_value": 0.7474889724, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "e2c5625551301d8e7d63f7975ff57a57", "metric_id": "99c1a5be147399d0276186241bc79a95", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "variance", "anomaly_score": -0.6693087378, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 0.6428140704, "min_metric_value": 0.576326103, "max_metric_value": 0.7474889724, "training_avg": 0.6619075377, "training_stddev": 0.02852714489, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last variance value is 0.643. The average for this metric is 0.662.", "is_anomalous": false}, {"value": 0.6918592965, "average": 0.6630595284, "min_value": 0.5773755041, "max_value": 0.7487435527, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "ee68975f2424e14a084cfbdc4a55a1bb", "metric_id": "cf12d0d5da8945d710882e0eecc7450e", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "variance", "anomaly_score": 1.00834788, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 0.6918592965, "min_metric_value": 0.5773755041, "max_metric_value": 0.7487435527, "training_avg": 0.6630595284, "training_stddev": 0.02856134142, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last variance value is 0.692. The average for this metric is 0.663.", "is_anomalous": false}, {"value": 0.7010050251, "average": 0.6644649172, "min_value": 0.5776355989, "max_value": 0.7512942354, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "a894c8cb7be201daf232670615c5d4a1", "metric_id": "a087e1d2dcc4e34bf4bae05c6a33dfc4", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "variance", "anomaly_score": 1.262480531, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 0.7010050251, "min_metric_value": 0.5776355989, "max_metric_value": 0.7512942354, "training_avg": 0.6644649172, "training_stddev": 0.02894310609, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last variance value is 0.701. The average for this metric is 0.664.", "is_anomalous": false}, {"value": 0.6998994975, "average": 0.6657304379, "min_value": 0.5781879636, "max_value": 0.7532729122, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "9a2895deebb3987d1222849c1ae73afb", "metric_id": "e7b831c15cdffebe47d7eb801910081e", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "variance", "anomaly_score": 1.170942215, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 0.6998994975, "min_metric_value": 0.5781879636, "max_metric_value": 0.7532729122, "training_avg": 0.6657304379, "training_stddev": 0.02918082477, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last variance value is 0.7. The average for this metric is 0.666.", "is_anomalous": false}, {"value": 0.6531658291, "average": 0.6652971755, "min_value": 0.5790476785, "max_value": 0.7515466725, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "07de79719ba3f82a1b0f946cc068e8ed", "metric_id": "77c75dc4c5d412acc29ace52df7337e7", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "variance", "anomaly_score": -0.4219623352, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 0.6531658291, "min_metric_value": 0.5790476785, "max_metric_value": 0.7515466725, "training_avg": 0.6652971755, "training_stddev": 0.02874983233, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last variance value is 0.653. The average for this metric is 0.665.", "is_anomalous": false}, {"value": 133.605, "average": 5.096620603, "min_value": 0.5790476785, "max_value": 0.7515466725, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "59f7c5d342fa40b26a9532344ec829d3", "metric_id": "39cdca140edba62d3c80957ac5718da1", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "variance", "anomaly_score": 5.294647803, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 133.605, "min_metric_value": -67.717502743, "max_metric_value": 77.910743949, "training_avg": 5.096620603, "training_stddev": 24.271374449, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last variance value is 133.605. The average for this metric is 5.097.", "is_anomalous": true}], "result_description": "In column STANDARD_DEVIATION, the last variance value is 133.605. The average for this metric is 5.097."}}, {"metadata": {"test_unique_id": "model.elementary_integration_tests.numeric_column_anomalies.elementary_volume_anomalies_numeric_column_anomalies_.volume_anomalies", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": null, "test_name": "volume_anomalies", "test_display_name": "Volume Anomalies", "latest_run_time": "2023-01-02T12:42:20+02:00", "latest_run_time_utc": "2023-01-02T10:42:20+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "row_count", "test_query": "select * from (\n \n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_VOLUME_ANOMALIES_NUMERIC_COLUMN_ANOMALIES___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n\n \n\n\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('row_count' as varchar)", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "The last row_count value is 0. The average for this metric is 193.333.", "result_query": "select * from (\n \n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_VOLUME_ANOMALIES_NUMERIC_COLUMN_ANOMALIES___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n\n \n\n\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('row_count' as varchar)"}, "configuration": {"test_name": "volume_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Row Count", "metrics": [{"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "cdbbc7767fe1465129e3cdb9040ed6b3", "metric_id": "fb6f5bd28fd9f0bd7d91c746323e3936", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "detected_at": "2023-01-02T10:42:19.545000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "0af9a026fe1b211470722a88285b9c88", "metric_id": "d7113b1c2062a36a46683329ef9120f5", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "detected_at": "2023-01-02T10:42:19.545000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "4a05bbf4d01f924619fa380d7922b1c6", "metric_id": "d2b17fd24bca8cbce8691cf902c232a7", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "detected_at": "2023-01-02T10:42:19.545000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "fec0aeeee66ce47a4e234509e04b5dd1", "metric_id": "c6d2c80845d27e7494525c7deb59edc6", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "detected_at": "2023-01-02T10:42:19.545000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "40336bfea5c415c167a453ec993ba874", "metric_id": "c6180c684e104e2352010a8d2a1a5b9a", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "detected_at": "2023-01-02T10:42:19.545000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "ec96cd183d4c9f13935e704ed1a06700", "metric_id": "eea7e6614b899cdf7c045a0c9867e7a1", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "detected_at": "2023-01-02T10:42:19.545000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "be0f9805ed93666e62b69862a54d5fd9", "metric_id": "75e6bd1fb1f987734746c2daae40515a", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "detected_at": "2023-01-02T10:42:19.545000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "e8381f74580b1ded4ccced3d66ab07e2", "metric_id": "d7c6dc1aab92461605ce3a665d6815a3", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "detected_at": "2023-01-02T10:42:19.545000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "b013586b46723895acba394c678c28c1", "metric_id": "af1a8baf51f2db0746a7efa8dc47c0a2", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "detected_at": "2023-01-02T10:42:19.545000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "5c50f22cf91866630a57bc5dd38a5f36", "metric_id": "e0aed087c4a5d0806da79168b48c8a4b", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "detected_at": "2023-01-02T10:42:19.545000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "1ad0124b04982b7f916c52605dc2d023", "metric_id": "935695602f9753523ba1a693abe04afd", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "detected_at": "2023-01-02T10:42:19.545000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "7464008aa812842301a5fbbda8487281", "metric_id": "b5c549b42b47c25973fdeff02e4e4c49", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "detected_at": "2023-01-02T10:42:19.545000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "9d8bf5e86989988ea781bf7468824c7f", "metric_id": "1b7d20ea9f7354a2939d0c17cc25beb7", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "detected_at": "2023-01-02T10:42:19.545000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "9bb968abb91d619f5c9366f01a5d7563", "metric_id": "ee99418db414f003f944e2d71ec6e788", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "detected_at": "2023-01-02T10:42:19.545000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "ed14266a968e5a98a427622a7129e55a", "metric_id": "289025b7a0ca86f1b3ae77a36e47bf56", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "detected_at": "2023-01-02T10:42:19.545000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "881deba81e38f4f30bf662d244758e31", "metric_id": "6cc6e32f4c638365a95d135c2548a1e2", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "detected_at": "2023-01-02T10:42:19.545000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "f7b8db4dd23676ea8ff19c336b0024ba", "metric_id": "7e00720524baf2b1c42503651e85a0fd", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "detected_at": "2023-01-02T10:42:19.545000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "d9dcb7b5e7ab3daa3268b953ef16028c", "metric_id": "db84ff85e5af9ff142bb1d79c15927be", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "detected_at": "2023-01-02T10:42:19.545000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "cadc63aeaae889c6a9118e64180dd26c", "metric_id": "02f92946708c476310f0ebe5ad94c817", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "detected_at": "2023-01-02T10:42:19.545000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "734f4afe06fe84e1c23139cd1f6513e3", "metric_id": "0f440ed8c791718c259a7f031f652c1a", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "detected_at": "2023-01-02T10:42:19.545000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "8832ceec19c31a88ba126e634271fd2a", "metric_id": "1af3e5a7cfaf42b5b97eb02e0d9ab555", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "detected_at": "2023-01-02T10:42:19.545000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "19d974a2e69e4a07ceb7259d1d7d7507", "metric_id": "1eb782b82e074913360a4e0f8955ecd9", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "detected_at": "2023-01-02T10:42:19.545000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "dc5a3ddad610c63bc70776a2b3e75aac", "metric_id": "8bc0e2ba050c5fa96e2a54adbcf7c99a", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "detected_at": "2023-01-02T10:42:19.545000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "321e7d89206348860e04c5f24630f1ed", "metric_id": "3b48f4a51c19a1a6b9bc96ede0114efa", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "detected_at": "2023-01-02T10:42:19.545000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "8efb8957a8554edb5514041cc45e898a", "metric_id": "853d4c7d7c6d815a67e5306b97fabe57", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "detected_at": "2023-01-02T10:42:19.545000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "9ccef4c00769d1ff390e850142ddb127", "metric_id": "580443b6c3a626705d7c3454541a1d83", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "detected_at": "2023-01-02T10:42:19.545000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "455068e57f74dd7cd9559056c1b08b8b", "metric_id": "390ff0dd8aafcc81b5c450709a643d55", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "detected_at": "2023-01-02T10:42:19.545000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "c56b1c51c6929a168f077e4ea20b43bd", "metric_id": "805fcccb8d5719f3003379649a1df3e7", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "detected_at": "2023-01-02T10:42:19.545000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 0.0, "average": 193.333333333, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "049a79d52aff168419b7375988afccc5", "metric_id": "dcc046473c5af34314099ac8f26bed3f", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "detected_at": "2023-01-02T10:42:19.545000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": -5.294651389, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 0.0, "min_metric_value": 83.788821832, "max_metric_value": 302.877844834, "training_avg": 193.333333333, "training_stddev": 36.514837167, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 0. The average for this metric is 193.333.", "is_anomalous": true}], "result_description": "The last row_count value is 0. The average for this metric is 193.333."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": "STANDARD_DEVIATION", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:25+02:00", "latest_run_time_utc": "2023-01-02T10:44:25+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "zero_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_standard_deviation__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('zero_percent' as varchar)\n and upper(column_name) = upper(cast('STANDARD_DEVIATION' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column STANDARD_DEVIATION, the last zero_percent value is 0. The average for this metric is 0.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_standard_deviation__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('zero_percent' as varchar)\n and upper(column_name) = upper(cast('STANDARD_DEVIATION' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Zero Percent", "metrics": [{"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "64c4b5218d2e3f8a85fa154bc2da6d9e", "metric_id": "08fe4af913bf5117ee80e8f57c4908a5", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "4670fd484da6cf423272e5b0ad9250c6", "metric_id": "5105ea31ea8bc91ffe22de31f019aa3d", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "7276a37409a397fcf59682fd0e64f4ed", "metric_id": "95bf37ad81879c974a00ccea3242f02a", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "c70b39448be8a92d23e6d7e832a26a5e", "metric_id": "c5202653ac0ec2fccca9e30ce7b2eabd", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "7dbe9a9ee5648d024890c907681c1cb1", "metric_id": "12ec87036a24b192ad687df653557757", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "3ed2653c05ead0a9473e5bc25717a3c8", "metric_id": "5c4980df7bad206281c4af13c73fea2a", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "554e470d6387ce1d29e8b08169bf8596", "metric_id": "40444e3e2b0c9786f938a36cfc7246b3", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "888cacc5101ea5edb265010fbd4caaa2", "metric_id": "1d49b22e47339d1edae573e15d287354", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "89c059806532ea26dd3f9dc882357bd1", "metric_id": "ae6031eb495022b5b842ce08f633b012", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "06a194fb9f7ceb02ff867e9a9c4c689d", "metric_id": "be106e3f086ca6b45aa1079bf060eebc", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "79265ddd712ebb7d966fe966aa838186", "metric_id": "a259a7c2ae89eb02e6c6ccd1a37742e6", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "1ee7452c618a126e1122bb908595d69a", "metric_id": "29b58861f795dd2b0dc94a68c7ab302d", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "ec15c031a3927b13859fbdad186d14c2", "metric_id": "365f76b1470fff0254a8d8dd9f8ea3cb", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "d4dc83b7aca91d3ae50d7ded5ed26e57", "metric_id": "6faf964d8693fa23abad2b2ca57e11f8", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "5f9b67ec9d706caa0fce36f2b6a4ecaa", "metric_id": "f4708a9a6577e71158e5f56b78015682", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "b2008a3502d825d9c6ba496509eaf2b8", "metric_id": "16249b8cf0891bc504311a175582456a", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "4c88cf6db3297b89472da05acdfd5162", "metric_id": "72dd6e66a39ef2bc1af53fb3b8b1ee38", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "5fd16b315a859b895abbe3027136cddd", "metric_id": "87fb9073cc12018a7e881df9a08efe7a", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "9c1e7bc99eeef97b92824b8ba523ee47", "metric_id": "3ad07758e960dc0aea0e3a9008867529", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "db8d885cc6a289d066cc5025bafc0d0a", "metric_id": "b0afe49a6acfb2f080dfbaf9babe2d9c", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "53a4fe6e7e6af4a0b462b976dfa37556", "metric_id": "c9298330afd0462973e5d260069d8158", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "fd1d688ed3a0e5e97a50e3a1d9a4e7c7", "metric_id": "6b4d008f4c782a9686295432baae6cc0", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "5c805ae6745521945e6dbcc12b411813", "metric_id": "4eca34adca8d712ab8b641d6c46b9d68", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "cecc76e1c5587c6b1c9e060a2d2f69c3", "metric_id": "c2debef11e5142da7b4e1590012a755d", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "1b74a016e7e54756e4ea359acefb63c9", "metric_id": "a6158b8e78f4606790f4840dd48ca9a0", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "cc8756f4b41b50d9a897acf639efccb4", "metric_id": "3585428590b6705589a7fcf7971f7034", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "082ae1a82c151da4cadffeb7e5b2ee76", "metric_id": "fca068604bc991bef17e24de222e51d8", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "b47d057296da6d65dff7b7b461f72cce", "metric_id": "6d4c0f7ff1e8b7785a4ad81808965dbe", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "c3963b2a9b35d4e832c030e46ca4b88c", "metric_id": "9e60cf8060307b89108c0231fa76c8b4", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_percent value is 0. The average for this metric is 0.", "is_anomalous": false}], "result_description": "In column STANDARD_DEVIATION, the last zero_percent value is 0. The average for this metric is 0."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": "AVERAGE", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:23+02:00", "latest_run_time_utc": "2023-01-02T10:44:23+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "min", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_min__average__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('min' as varchar)\n and upper(column_name) = upper(cast('AVERAGE' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column AVERAGE, the last min value is 101. The average for this metric is 99.067.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_min__average__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('min' as varchar)\n and upper(column_name) = upper(cast('AVERAGE' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Min", "metrics": [{"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "5b7cdd3474d1e403626affd13dead93b", "metric_id": "01a15beccb1e2ff61429e0e5dbe3ab8e", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "detected_at": "2023-01-02T10:44:22.441000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "8a9f04940f3aa3aa6faa6e03d80c74f3", "metric_id": "26fb6a63759fdecaef58f08bb40d87c6", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "detected_at": "2023-01-02T10:44:22.441000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "fc24f25c1a489d402e585669ac82be67", "metric_id": "72520a258581f9fc179b081d3e8f38a8", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "detected_at": "2023-01-02T10:44:22.441000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "71508816449cdbedffc33bc70950b999", "metric_id": "4ea5aef11ebf0a3101fab51f8bfde817", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "detected_at": "2023-01-02T10:44:22.441000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "3b9dac84df7548ede2faa93931560989", "metric_id": "243b5a27da2b016a551a42efa244fd54", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "detected_at": "2023-01-02T10:44:22.441000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "d810573585b8a3f9feee6a848338c6b5", "metric_id": "f19cdb02c859b25d5ff0c84a89f65766", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "detected_at": "2023-01-02T10:44:22.441000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "e2e5ba24eb5741a76a97e4ad9be71202", "metric_id": "dc41c5956d109623ca7414fd56612f46", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "detected_at": "2023-01-02T10:44:22.441000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "95dc730bc42a2dc1b4addc95f58c04bb", "metric_id": "2549d5ea1839c81b0a4c7ad7dfa9adfe", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "detected_at": "2023-01-02T10:44:22.441000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "d8d91a69495c4e3b49c051775c46d411", "metric_id": "836797efde941330485a9b5c7a3b5510", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "detected_at": "2023-01-02T10:44:22.441000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "9e550826268034605f1355802258a9c5", "metric_id": "fdabd9e5fd87bcc4d2f205b4dc9fbea2", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "detected_at": "2023-01-02T10:44:22.441000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "d84cc05e221b52f30729dca5ed76bb3d", "metric_id": "87c39cb2548915619fc3c45ae6a1dd55", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "detected_at": "2023-01-02T10:44:22.441000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "26369ba2f84d65755ef2ac92614eb89d", "metric_id": "8cb7e1c93ed79dd7a506d126cd5be1ac", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "detected_at": "2023-01-02T10:44:22.441000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "f292b948fbad15313ce6cf064b582ac2", "metric_id": "b7dae1781a257e679a4380aa4c568305", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "detected_at": "2023-01-02T10:44:22.441000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "758b8fc18a8499d897a5f9a21fbd0c04", "metric_id": "d699fe224ff1b0edb0166883813ebcfc", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "detected_at": "2023-01-02T10:44:22.441000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "2a2c11e53f76fae7ca1b9a75f62fbd30", "metric_id": "37989128bca19c6ae1709bb924a57646", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "detected_at": "2023-01-02T10:44:22.441000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "94f9a9123772391a3990a7d5088fc2f7", "metric_id": "75b0613a37d85b5173011dafa5df92e0", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "detected_at": "2023-01-02T10:44:22.441000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "5dab20e8cede14226eeeefa4f9a0145c", "metric_id": "6d76c15a68e64ba80e7bb6deaf88e52c", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "detected_at": "2023-01-02T10:44:22.441000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "aa75ab7a3b1950173ad6dea4ed3047b8", "metric_id": "bff91cb94498e469270399832f5a6c78", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "detected_at": "2023-01-02T10:44:22.441000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "a367cbf17d0a3808b9a0f3bbc7153a60", "metric_id": "669634376771374610a2fda00bf47463", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "detected_at": "2023-01-02T10:44:22.441000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "affed583c6cfd5064ecbea88295e8e9a", "metric_id": "67ab52b4c8b2e5e819578120708d0075", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "detected_at": "2023-01-02T10:44:22.441000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "814084760828028bf28fac8ebb44dcdc", "metric_id": "b65c25e3f4786a8bf343ab7ab588cc95", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "detected_at": "2023-01-02T10:44:22.441000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "007c00e06a8e61698e7115eecade86f8", "metric_id": "47d090dc72610d9e01d75cf1f8a2cd72", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "detected_at": "2023-01-02T10:44:22.441000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "fc39d417ae9be4f12363d32f8f6e8df5", "metric_id": "c75e7100afa74d64eff2948020fc7309", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "detected_at": "2023-01-02T10:44:22.441000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "d40ce2b53896533a2e96b6efaaad6048", "metric_id": "aa73f41274ba4a38e916650d920c954b", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "detected_at": "2023-01-02T10:44:22.441000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "e04f068da45eb7bfe84933496f9c5017", "metric_id": "6ccda4d1f3314dbfbb941346c6c8b8ed", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "detected_at": "2023-01-02T10:44:22.441000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "eb7bad0cacafb63408c998e61beaa5d0", "metric_id": "61ba440d72d2e0b5bcef09d69c795791", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "detected_at": "2023-01-02T10:44:22.441000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "a8a843fe1663e425b5476867837b0976", "metric_id": "42340a9173a6770e41ffd722015ba215", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "detected_at": "2023-01-02T10:44:22.441000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "3f122ee2c6c07ee39acba9b815e51db1", "metric_id": "1bac39e0ae00d9db2fa4f9720810a208", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "detected_at": "2023-01-02T10:44:22.441000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 101.0, "average": 99.066666667, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "b3457e5e3e77a5692e2d9439c465b95a", "metric_id": "2d3145f295fe63a3dcd5739d752d18f9", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "detected_at": "2023-01-02T10:44:22.441000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "min", "anomaly_score": 5.294651389, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 101.0, "min_metric_value": 97.971221552, "max_metric_value": 100.162111782, "training_avg": 99.066666667, "training_stddev": 0.3651483717, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last min value is 101. The average for this metric is 99.067.", "is_anomalous": true}], "result_description": "In column AVERAGE, the last min value is 101. The average for this metric is 99.067."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": "ZERO_PERCENT", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:24+02:00", "latest_run_time_utc": "2023-01-02T10:44:24+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "zero_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_zero_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('zero_percent' as varchar)\n and upper(column_name) = upper(cast('ZERO_PERCENT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column ZERO_PERCENT, the last zero_percent value is 61. The average for this metric is 21.25.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_zero_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('zero_percent' as varchar)\n and upper(column_name) = upper(cast('ZERO_PERCENT' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Zero Percent", "metrics": [{"value": 23.0, "average": 22.75, "min_value": 21.689339828, "max_value": 23.810660172, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "8dfb24b344230e6f5bdb58dfc85a7d8a", "metric_id": "02b9b60541213242be267223ab139d7b", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_percent", "anomaly_score": 0.7071067812, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 23.0, "min_metric_value": 21.689339828, "max_metric_value": 23.810660172, "training_avg": 22.75, "training_stddev": 0.3535533906, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_percent value is 23. The average for this metric is 22.75.", "is_anomalous": false}, {"value": 20.5, "average": 22.0, "min_value": 18.031373033, "max_value": 25.968626967, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "1deac76fb816816a216d44a8e7f91640", "metric_id": "520073277d4e906ac0c15bb2e59f516b", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_percent", "anomaly_score": -1.133893419, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 20.5, "min_metric_value": 18.031373033, "max_metric_value": 25.968626967, "training_avg": 22.0, "training_stddev": 1.322875656, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_percent value is 20.5. The average for this metric is 22.", "is_anomalous": false}, {"value": 21.5, "average": 21.875, "min_value": 18.548966326, "max_value": 25.201033674, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "57886176903d8e455723c7c55a45b1d3", "metric_id": "48599e299335ccc76f9cd5317051d814", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_percent", "anomaly_score": -0.3382407126, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 21.5, "min_metric_value": 18.548966326, "max_metric_value": 25.201033674, "training_avg": 21.875, "training_stddev": 1.108677891, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_percent value is 21.5. The average for this metric is 21.875.", "is_anomalous": false}, {"value": 20.5, "average": 21.6, "min_value": 18.179473725, "max_value": 25.020526275, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "761afc249b05943d06ac98ef8f219e54", "metric_id": "8392ccf876de7f78b30cc3a3741859b0", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_percent", "anomaly_score": -0.9647638212, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 20.5, "min_metric_value": 18.179473725, "max_metric_value": 25.020526275, "training_avg": 21.6, "training_stddev": 1.140175425, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_percent value is 20.5. The average for this metric is 21.6.", "is_anomalous": false}, {"value": 19.5, "average": 21.25, "min_value": 17.253126222, "max_value": 25.246873778, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "f7faa770e740c132fe8bebab52c3e127", "metric_id": "80aa0bcd22a14240ac68df4aea971cdf", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_percent", "anomaly_score": -1.313526594, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 19.5, "min_metric_value": 17.253126222, "max_metric_value": 25.246873778, "training_avg": 21.25, "training_stddev": 1.332291259, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_percent value is 19.5. The average for this metric is 21.25.", "is_anomalous": false}, {"value": 21.5, "average": 21.285714286, "min_value": 17.626089012, "max_value": 24.945339559, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "ffaaa7f020827940e83a04e36a048f86", "metric_id": "80fa19933a64eefe77dee5d7046ac4d1", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_percent", "anomaly_score": 0.1756620131, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 21.5, "min_metric_value": 17.626089012, "max_metric_value": 24.945339559, "training_avg": 21.285714286, "training_stddev": 1.219875091, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_percent value is 21.5. The average for this metric is 21.286.", "is_anomalous": false}, {"value": 17.0, "average": 20.75, "min_value": 15.080532905, "max_value": 26.419467095, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "03225430c8efd8e90dd1c343e868bd13", "metric_id": "b2ac5b70b206d1b33ddbe5c10851c72d", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_percent", "anomaly_score": -1.984313483, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 17.0, "min_metric_value": 15.080532905, "max_metric_value": 26.419467095, "training_avg": 20.75, "training_stddev": 1.889822365, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_percent value is 17. The average for this metric is 20.75.", "is_anomalous": false}, {"value": 22.5, "average": 20.944444444, "min_value": 15.359867469, "max_value": 26.52902142, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "3f33f0fec776c9802150a0622e56c130", "metric_id": "c351534a5bcf7a5a39e2975771432172", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_percent", "anomaly_score": 0.8356347646, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 22.5, "min_metric_value": 15.359867469, "max_metric_value": 26.52902142, "training_avg": 20.944444444, "training_stddev": 1.861525659, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_percent value is 22.5. The average for this metric is 20.944.", "is_anomalous": false}, {"value": 22.0, "average": 21.05, "min_value": 15.690429122, "max_value": 26.409570878, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "579bdb28bfd8abe6520cc522cf20382f", "metric_id": "da81dec767057fa5a94c335b730fcc6e", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_percent", "anomaly_score": 0.5317589905, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 22.0, "min_metric_value": 15.690429122, "max_metric_value": 26.409570878, "training_avg": 21.05, "training_stddev": 1.786523626, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_percent value is 22. The average for this metric is 21.05.", "is_anomalous": false}, {"value": 19.0, "average": 20.863636364, "min_value": 15.451528725, "max_value": 26.275744002, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "bfb773cd11d01eeb21410420899e154d", "metric_id": "bfb665e1178650084063f484357d8c5d", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_percent", "anomaly_score": -1.033037305, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 19.0, "min_metric_value": 15.451528725, "max_metric_value": 26.275744002, "training_avg": 20.863636364, "training_stddev": 1.80403588, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_percent value is 19. The average for this metric is 20.864.", "is_anomalous": false}, {"value": 22.5, "average": 21.0, "min_value": 15.64870449, "max_value": 26.35129551, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "ce572a66704957aac786e2f357680e13", "metric_id": "216b914f774f3e66113e9096fd38f492", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_percent", "anomaly_score": 0.8409178659, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 22.5, "min_metric_value": 15.64870449, "max_metric_value": 26.35129551, "training_avg": 21.0, "training_stddev": 1.78376517, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_percent value is 22.5. The average for this metric is 21.", "is_anomalous": false}, {"value": 22.0, "average": 21.076923077, "min_value": 15.886325068, "max_value": 26.267521086, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "b05b96b7497a95e9b88e9492bccce745", "metric_id": "1bd3a35b986f9cbabdd5c86495bed418", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_percent", "anomaly_score": 0.5335090031, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 22.0, "min_metric_value": 15.886325068, "max_metric_value": 26.267521086, "training_avg": 21.076923077, "training_stddev": 1.730199336, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_percent value is 22. The average for this metric is 21.077.", "is_anomalous": false}, {"value": 20.5, "average": 21.035714286, "min_value": 16.027342174, "max_value": 26.044086397, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "ab350cfff7034a236bcdac0f7741a558", "metric_id": "18ac5bf326b98b28656ffbc09cb8201d", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_percent", "anomaly_score": -0.3208912639, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 20.5, "min_metric_value": 16.027342174, "max_metric_value": 26.044086397, "training_avg": 21.035714286, "training_stddev": 1.669457371, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_percent value is 20.5. The average for this metric is 21.036.", "is_anomalous": false}, {"value": 22.0, "average": 21.1, "min_value": 16.216353821, "max_value": 25.983646179, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "cdee44113cc701125257211b99333379", "metric_id": "78e146033852242b4cca90fbe12f1cf4", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_percent", "anomaly_score": 0.5528656052, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 22.0, "min_metric_value": 16.216353821, "max_metric_value": 25.983646179, "training_avg": 21.1, "training_stddev": 1.62788206, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_percent value is 22. The average for this metric is 21.1.", "is_anomalous": false}, {"value": 21.0, "average": 21.09375, "min_value": 16.375103478, "max_value": 25.812396522, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "71a3fcb5b8115ca006fa2769b3a0fd6a", "metric_id": "42bd955268b2bb111d65b2bf318193de", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_percent", "anomaly_score": -0.05960395607, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 21.0, "min_metric_value": 16.375103478, "max_metric_value": 25.812396522, "training_avg": 21.09375, "training_stddev": 1.572882174, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_percent value is 21. The average for this metric is 21.094.", "is_anomalous": false}, {"value": 17.5, "average": 20.882352941, "min_value": 15.618191369, "max_value": 26.146514513, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "557e5beac14d2df5ac2227e51183b79c", "metric_id": "ddee19131a64d391d8af10f53a354c19", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_percent", "anomaly_score": -1.927573591, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 17.5, "min_metric_value": 15.618191369, "max_metric_value": 26.146514513, "training_avg": 20.882352941, "training_stddev": 1.754720524, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_percent value is 17.5. The average for this metric is 20.882.", "is_anomalous": false}, {"value": 19.0, "average": 20.777777778, "min_value": 15.500189202, "max_value": 26.055366354, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "58c6d1f4cf7c1c781b6aaa74cf5ff64d", "metric_id": "451a476d9f620aded8af680bea6f2222", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_percent", "anomaly_score": -1.010562543, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 19.0, "min_metric_value": 15.500189202, "max_metric_value": 26.055366354, "training_avg": 20.777777778, "training_stddev": 1.759196192, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_percent value is 19. The average for this metric is 20.778.", "is_anomalous": false}, {"value": 14.5, "average": 20.447368421, "min_value": 13.741126239, "max_value": 27.153610604, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "499b7e36f4b868f10fafa9a704b25146", "metric_id": "2f138dab482dea5adc4a1d1c1d2ef778", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_percent", "anomaly_score": -2.66052206, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 14.5, "min_metric_value": 13.741126239, "max_metric_value": 27.153610604, "training_avg": 20.447368421, "training_stddev": 2.235414061, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_percent value is 14.5. The average for this metric is 20.447.", "is_anomalous": false}, {"value": 19.0, "average": 20.375, "min_value": 13.775807466, "max_value": 26.974192534, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "ea6bc5638327c4b23043f09d4890be53", "metric_id": "eceb37ec2e1510e35fdc030d56bc54a9", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_percent", "anomaly_score": -0.6250764739, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 19.0, "min_metric_value": 13.775807466, "max_metric_value": 26.974192534, "training_avg": 20.375, "training_stddev": 2.199730845, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_percent value is 19. The average for this metric is 20.375.", "is_anomalous": false}, {"value": 14.5, "average": 20.095238095, "min_value": 12.600954559, "max_value": 27.589521631, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "85048925c3b5dfa257f78d3b3e299dfd", "metric_id": "9026e62df23ea55e96fe23e4d301971f", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_percent", "anomaly_score": -2.239802405, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 14.5, "min_metric_value": 12.600954559, "max_metric_value": 27.589521631, "training_avg": 20.095238095, "training_stddev": 2.498094512, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_percent value is 14.5. The average for this metric is 20.095.", "is_anomalous": false}, {"value": 20.5, "average": 20.113636364, "min_value": 12.795383964, "max_value": 27.431888763, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "a1d70ccabd45b78d412f9ddc7ae1aefe", "metric_id": "08eed320a207e94c15877c5d72033e88", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_percent", "anomaly_score": 0.1583835656, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 20.5, "min_metric_value": 12.795383964, "max_metric_value": 27.431888763, "training_avg": 20.113636364, "training_stddev": 2.439417466, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_percent value is 20.5. The average for this metric is 20.114.", "is_anomalous": false}, {"value": 16.0, "average": 19.934782609, "min_value": 12.335830632, "max_value": 27.533734586, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "ca167e2f902ebebd695941eea0691b42", "metric_id": "925c98bdd924c7c55bedba1231905456", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_percent", "anomaly_score": -1.553417874, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 16.0, "min_metric_value": 12.335830632, "max_metric_value": 27.533734586, "training_avg": 19.934782609, "training_stddev": 2.532983992, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_percent value is 16. The average for this metric is 19.935.", "is_anomalous": false}, {"value": 18.5, "average": 19.875, "min_value": 12.391322109, "max_value": 27.358677891, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "5ac3810af3923b8f7256edd6a8153f31", "metric_id": "27bda0b7707b3346563ca95add73013b", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_percent", "anomaly_score": -0.5511995652, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 18.5, "min_metric_value": 12.391322109, "max_metric_value": 27.358677891, "training_avg": 19.875, "training_stddev": 2.494559297, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_percent value is 18.5. The average for this metric is 19.875.", "is_anomalous": false}, {"value": 19.0, "average": 19.84, "min_value": 12.495103813, "max_value": 27.184896187, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "1201ebb85f85041c2468135a1a3620b0", "metric_id": "c34968aa0e4dda2b778782e7c3cbf15f", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_percent", "anomaly_score": -0.343095387, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 19.0, "min_metric_value": 12.495103813, "max_metric_value": 27.184896187, "training_avg": 19.84, "training_stddev": 2.448298729, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_percent value is 19. The average for this metric is 19.84.", "is_anomalous": false}, {"value": 20.5, "average": 19.865384615, "min_value": 12.658416833, "max_value": 27.072352398, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "4ea14265ee92d1b67ed1909e73bdd0f5", "metric_id": "3782b9b8c1955f02682c54c905ae242f", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_percent", "anomaly_score": 0.2641674295, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 20.5, "min_metric_value": 12.658416833, "max_metric_value": 27.072352398, "training_avg": 19.865384615, "training_stddev": 2.402322594, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_percent value is 20.5. The average for this metric is 19.865.", "is_anomalous": false}, {"value": 23.0, "average": 19.981481481, "min_value": 12.68641817, "max_value": 27.276544793, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "3ea7a165023c88eaa1579f01deecb397", "metric_id": "f32d5a9c43ef14c6503724df31c5d175", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_percent", "anomaly_score": 1.24132652, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 23.0, "min_metric_value": 12.68641817, "max_metric_value": 27.276544793, "training_avg": 19.981481481, "training_stddev": 2.43168777, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_percent value is 23. The average for this metric is 19.981.", "is_anomalous": false}, {"value": 14.5, "average": 19.785714286, "min_value": 11.981563986, "max_value": 27.589864586, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "935aa4274cadad73845ce52860cb8ff1", "metric_id": "fbde01d49e9b6b7d0a25ce4556de3897", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_percent", "anomaly_score": -2.031885887, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 14.5, "min_metric_value": 11.981563986, "max_metric_value": 27.589864586, "training_avg": 19.785714286, "training_stddev": 2.601383433, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_percent value is 14.5. The average for this metric is 19.786.", "is_anomalous": false}, {"value": 22.5, "average": 19.879310345, "min_value": 12.06803581, "max_value": 27.690584879, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "ddda045d49fa4c5f7ec992cca2fe6783", "metric_id": "63cab68299e695a5c3e93a1703375c4a", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_percent", "anomaly_score": 1.006502707, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 22.5, "min_metric_value": 12.06803581, "max_metric_value": 27.690584879, "training_avg": 19.879310345, "training_stddev": 2.603758178, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_percent value is 22.5. The average for this metric is 19.879.", "is_anomalous": false}, {"value": 61.0, "average": 21.25, "min_value": 12.06803581, "max_value": 27.690584879, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "9b04c9cafec2ac401dd5420536a3957c", "metric_id": "73863dadf75736729fef20e428afee4e", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_percent", "anomaly_score": 5.011630871, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 61.0, "min_metric_value": -2.5446495, "max_metric_value": 45.0446495, "training_avg": 21.25, "training_stddev": 7.931549833, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_percent value is 61. The average for this metric is 21.25.", "is_anomalous": true}], "result_description": "In column ZERO_PERCENT, the last zero_percent value is 61. The average for this metric is 21.25."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": "STANDARD_DEVIATION", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:25+02:00", "latest_run_time_utc": "2023-01-02T10:44:25+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "average", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_standard_deviation__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('STANDARD_DEVIATION' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column STANDARD_DEVIATION, the last average value is 99.555. The average for this metric is 99.97.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_standard_deviation__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('STANDARD_DEVIATION' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Average", "metrics": [{"value": 99.945, "average": 99.985, "min_value": 99.815294373, "max_value": 100.154705627, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "e77dc540277a3bda14c6e4301f343f25", "metric_id": "ee623213cb31309147abae4c7da0afa2", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "average", "anomaly_score": -0.7071067813, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 99.945, "min_metric_value": 99.815294373, "max_metric_value": 100.154705627, "training_avg": 99.985, "training_stddev": 0.05656854249, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last average value is 99.945. The average for this metric is 99.985.", "is_anomalous": false}, {"value": 99.89, "average": 99.953333333, "min_value": 99.749679215, "max_value": 100.156987452, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "538b68781c4066328b75c5b36bdabf5e", "metric_id": "224681d539c82844e4b159f7e2a5175c", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "average", "anomaly_score": -0.9329543709, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 99.89, "min_metric_value": 99.749679215, "max_metric_value": 100.156987452, "training_avg": 99.953333333, "training_stddev": 0.06788470617, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last average value is 99.89. The average for this metric is 99.953.", "is_anomalous": false}, {"value": 99.875, "average": 99.93375, "min_value": 99.730141921, "max_value": 100.137358079, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "f7458ea02755e7e79a4672f970720bc0", "metric_id": "ccdcdae72d7e26ac6b9b23c5995d025d", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "average", "anomaly_score": -0.8656336258, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 99.875, "min_metric_value": 99.730141921, "max_metric_value": 100.137358079, "training_avg": 99.93375, "training_stddev": 0.0678693598, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last average value is 99.875. The average for this metric is 99.934.", "is_anomalous": false}, {"value": 99.985, "average": 99.944, "min_value": 99.754738276, "max_value": 100.133261724, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "c470494086c6e376c4d8a292e2fc000c", "metric_id": "5c52a237dabd3a144e97934675261602", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "average", "anomaly_score": 0.6498936907, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 99.985, "min_metric_value": 99.754738276, "max_metric_value": 100.133261724, "training_avg": 99.944, "training_stddev": 0.06308724117, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last average value is 99.985. The average for this metric is 99.944.", "is_anomalous": false}, {"value": 100.07, "average": 99.965, "min_value": 99.73593669, "max_value": 100.19406331, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "602617eac133d381fecd9aff9565de82", "metric_id": "a77bff5299f3e043550fb8e8ddee5e50", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "average", "anomaly_score": 1.375165669, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 100.07, "min_metric_value": 99.73593669, "max_metric_value": 100.19406331, "training_avg": 99.965, "training_stddev": 0.0763544367, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last average value is 100.07. The average for this metric is 99.965.", "is_anomalous": false}, {"value": 100.055, "average": 99.977857143, "min_value": 99.74517864, "max_value": 100.210535646, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "aa6a94007a37eee264badb9b53ebc095", "metric_id": "b783cfd3f6c6414528a76cc0b3f338bc", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "average", "anomaly_score": 0.9946280751, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 100.055, "min_metric_value": 99.74517864, "max_metric_value": 100.210535646, "training_avg": 99.977857143, "training_stddev": 0.07755950096, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last average value is 100.055. The average for this metric is 99.978.", "is_anomalous": false}, {"value": 99.94, "average": 99.973125, "min_value": 99.753996246, "max_value": 100.192253754, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "544baddfc5f9e0d1c2ea5e8231b15074", "metric_id": "6aa42e073352d07f65809d74c0f6409c", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "average", "anomaly_score": -0.453500503, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 99.94, "min_metric_value": 99.753996246, "max_metric_value": 100.192253754, "training_avg": 99.973125, "training_stddev": 0.07304291789, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last average value is 99.94. The average for this metric is 99.973.", "is_anomalous": false}, {"value": 100.025, "average": 99.978888889, "min_value": 99.767450364, "max_value": 100.190327414, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "554e248ff075dfdd7ee5f8c591e0dca5", "metric_id": "a794c7847d1c216a0d122fea9d4788fb", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "average", "anomaly_score": 0.6542484779, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 100.025, "min_metric_value": 99.767450364, "max_metric_value": 100.190327414, "training_avg": 99.978888889, "training_stddev": 0.07047950843, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last average value is 100.025. The average for this metric is 99.979.", "is_anomalous": false}, {"value": 99.89, "average": 99.97, "min_value": 99.753551392, "max_value": 100.186448608, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "af9b409c2521f75bb4767ab5ad1a9c83", "metric_id": "dffbc39e9a4b4935a91460270b3e8019", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "average", "anomaly_score": -1.108808239, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 99.89, "min_metric_value": 99.753551392, "max_metric_value": 100.186448608, "training_avg": 99.97, "training_stddev": 0.07214953607, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last average value is 99.89. The average for this metric is 99.97.", "is_anomalous": false}, {"value": 100.02, "average": 99.974545455, "min_value": 99.764282632, "max_value": 100.184808277, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "85df316427da2a85603332ec632257a6", "metric_id": "f07207667be6ab8d63203900d012fd6c", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "average", "anomaly_score": 0.6485389794, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 100.02, "min_metric_value": 99.764282632, "max_metric_value": 100.184808277, "training_avg": 99.974545455, "training_stddev": 0.07008760752, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last average value is 100.02. The average for this metric is 99.975.", "is_anomalous": false}, {"value": 100.03, "average": 99.979166667, "min_value": 99.773016898, "max_value": 100.185316436, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "03d729d5a518898cb424454bf8536950", "metric_id": "ba8495ffc52410e2dee34f125700c5df", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "average", "anomaly_score": 0.7397534361, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 100.03, "min_metric_value": 99.773016898, "max_metric_value": 100.185316436, "training_avg": 99.979166667, "training_stddev": 0.06871658968, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last average value is 100.03. The average for this metric is 99.979.", "is_anomalous": false}, {"value": 99.94, "average": 99.976153846, "min_value": 99.776108178, "max_value": 100.176199514, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "c2bf1c38a720054326c2331dd7ee2f86", "metric_id": "8a4596549bff8ba9d64e846e6742516a", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "average", "anomaly_score": -0.5421838905, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 99.94, "min_metric_value": 99.776108178, "max_metric_value": 100.176199514, "training_avg": 99.976153846, "training_stddev": 0.06668188928, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last average value is 99.94. The average for this metric is 99.976.", "is_anomalous": false}, {"value": 100.035, "average": 99.980357143, "min_value": 99.782452945, "max_value": 100.17826134, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "e4aa7d2cec1730344f949be9ac474e3d", "metric_id": "956dffb158f05f912b815d8b1608a01d", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "average", "anomaly_score": 0.8283228623, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 100.035, "min_metric_value": 99.782452945, "max_metric_value": 100.17826134, "training_avg": 99.980357143, "training_stddev": 0.06596806587, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last average value is 100.035. The average for this metric is 99.98.", "is_anomalous": false}, {"value": 100.06, "average": 99.985666667, "min_value": 99.785231426, "max_value": 100.186101907, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "23cb2fab90961ee4e1a8aa5dfe337348", "metric_id": "14fa277fa1c50e179f19b1335eb04f99", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "average", "anomaly_score": 1.112578802, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 100.06, "min_metric_value": 99.785231426, "max_metric_value": 100.186101907, "training_avg": 99.985666667, "training_stddev": 0.06681174691, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last average value is 100.06. The average for this metric is 99.986.", "is_anomalous": false}, {"value": 100.025, "average": 99.988125, "min_value": 99.792251954, "max_value": 100.183998046, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "8e4b9a402ce3346edc71a88301cdbc08", "metric_id": "8b973140ac3fd1adda82a1364477befa", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "average", "anomaly_score": 0.5647790878, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 100.025, "min_metric_value": 99.792251954, "max_metric_value": 100.183998046, "training_avg": 99.988125, "training_stddev": 0.06529101519, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last average value is 100.025. The average for this metric is 99.988.", "is_anomalous": false}, {"value": 99.98, "average": 99.987647059, "min_value": 99.79790168, "max_value": 100.177392438, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "a7458f9982b0b07cfadc5ee32b559894", "metric_id": "c3280d95e94e6ea0f1de0537d35bfccf", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "average", "anomaly_score": -0.1209050603, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 99.98, "min_metric_value": 99.79790168, "max_metric_value": 100.177392438, "training_avg": 99.987647059, "training_stddev": 0.06324845962, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last average value is 99.98. The average for this metric is 99.988.", "is_anomalous": false}, {"value": 99.985, "average": 99.9875, "min_value": 99.803410428, "max_value": 100.171589572, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "88ca5594549c6d72797a80cd815c027e", "metric_id": "b46d43fc60f339bc35ecadd45d43d939", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "average", "anomaly_score": -0.04074103661, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 99.985, "min_metric_value": 99.803410428, "max_metric_value": 100.171589572, "training_avg": 99.9875, "training_stddev": 0.06136319073, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last average value is 99.985. The average for this metric is 99.988.", "is_anomalous": false}, {"value": 100.005, "average": 99.988421053, "min_value": 99.809113173, "max_value": 100.167728932, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "c4b6bb3881b6b5e9d4191e4c5e24d1f9", "metric_id": "422717596a49457d82caec12a2cc0f2e", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "average", "anomaly_score": 0.2773823558, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 100.005, "min_metric_value": 99.809113173, "max_metric_value": 100.167728932, "training_avg": 99.988421053, "training_stddev": 0.05976929326, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last average value is 100.005. The average for this metric is 99.988.", "is_anomalous": false}, {"value": 100.115, "average": 99.99475, "min_value": 99.800664594, "max_value": 100.188835406, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "7b5460d2472f593c8c9b496886fd54ac", "metric_id": "c969f80dd70f88de850be4db942d2f52", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "average", "anomaly_score": 1.858717809, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 100.115, "min_metric_value": 99.800664594, "max_metric_value": 100.188835406, "training_avg": 99.99475, "training_stddev": 0.06469513523, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last average value is 100.115. The average for this metric is 99.995.", "is_anomalous": false}, {"value": 99.905, "average": 99.99047619, "min_value": 99.792390711, "max_value": 100.18856167, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "07da03b39c0a00b5ebf07c7c9e864bc2", "metric_id": "ee1b85820e8252a112d01596528f4d6c", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "average", "anomaly_score": -1.294534927, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 99.905, "min_metric_value": 99.792390711, "max_metric_value": 100.18856167, "training_avg": 99.99047619, "training_stddev": 0.0660284931, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last average value is 99.905. The average for this metric is 99.99.", "is_anomalous": false}, {"value": 99.955, "average": 99.988863636, "min_value": 99.79422486, "max_value": 100.183502413, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "6fa41d92cde0995e7c2e5df57fa9e972", "metric_id": "955a71ca2e0686b27dc10eb9a7a5f15b", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "average", "anomaly_score": -0.521945889, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 99.955, "min_metric_value": 99.79422486, "max_metric_value": 100.183502413, "training_avg": 99.988863636, "training_stddev": 0.06487959208, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last average value is 99.955. The average for this metric is 99.989.", "is_anomalous": false}, {"value": 99.895, "average": 99.984782609, "min_value": 99.785760543, "max_value": 100.183804674, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "69a50ba7a1800c645b55cdca39f5f4f2", "metric_id": "9ba0f20ca301d7bd248a34a2f23049c5", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "average", "anomaly_score": -1.3533566, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 99.895, "min_metric_value": 99.785760543, "max_metric_value": 100.183804674, "training_avg": 99.984782609, "training_stddev": 0.06634068853, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last average value is 99.895. The average for this metric is 99.985.", "is_anomalous": false}, {"value": 99.95, "average": 99.983333333, "min_value": 99.787523976, "max_value": 100.179142691, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "24b61f3601c4720099d73992634d5e53", "metric_id": "4ff67164f345292778ffe1afa124c9fc", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "average", "anomaly_score": -0.5107008237, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 99.95, "min_metric_value": 99.787523976, "max_metric_value": 100.179142691, "training_avg": 99.983333333, "training_stddev": 0.06526978573, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last average value is 99.95. The average for this metric is 99.983.", "is_anomalous": false}, {"value": 100.02, "average": 99.9848, "min_value": 99.79185506, "max_value": 100.17774494, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "af062f815f42e12d26e7f2e5af9ca9c1", "metric_id": "97ccce01e47c989b4597f87fc83bf545", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "average", "anomaly_score": 0.5473063964, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 100.02, "min_metric_value": 99.79185506, "max_metric_value": 100.17774494, "training_avg": 99.9848, "training_stddev": 0.06431498011, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last average value is 100.02. The average for this metric is 99.985.", "is_anomalous": false}, {"value": 99.96, "average": 99.983846154, "min_value": 99.794237245, "max_value": 100.173455063, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "059dfe5e3ad587115ea7a43a4df215bd", "metric_id": "67cb1840f2409b64417140cdbf5208aa", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "average", "anomaly_score": -0.3772948319, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 99.96, "min_metric_value": 99.794237245, "max_metric_value": 100.173455063, "training_avg": 99.983846154, "training_stddev": 0.06320296975, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last average value is 99.96. The average for this metric is 99.984.", "is_anomalous": false}, {"value": 100.05, "average": 99.986296296, "min_value": 99.796487013, "max_value": 100.17610558, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "9c008a078c01ad5df7ab62bb17400155", "metric_id": "959e3474e7760cf214df9d0d39e7d4de", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "average", "anomaly_score": 1.006858609, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 100.05, "min_metric_value": 99.796487013, "max_metric_value": 100.17610558, "training_avg": 99.986296296, "training_stddev": 0.06326976116, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last average value is 100.05. The average for this metric is 99.986.", "is_anomalous": false}, {"value": 99.94, "average": 99.984642857, "min_value": 99.796541441, "max_value": 100.172744274, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "73400abdaf407cf4499f4ab9126f830a", "metric_id": "555f8ffe4d39ffadbfba96f7dfad515e", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "average", "anomaly_score": -0.7120019296, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 99.94, "min_metric_value": 99.796541441, "max_metric_value": 100.172744274, "training_avg": 99.984642857, "training_stddev": 0.06270047213, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last average value is 99.94. The average for this metric is 99.985.", "is_anomalous": false}, {"value": 99.99, "average": 99.984827586, "min_value": 99.800091554, "max_value": 100.169563618, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "34686c84078041fee5fdaa802f285099", "metric_id": "23cd85022f0cf6964a34782901e759b4", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "average", "anomaly_score": 0.08399683169, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 99.99, "min_metric_value": 99.800091554, "max_metric_value": 100.169563618, "training_avg": 99.984827586, "training_stddev": 0.0615786773, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last average value is 99.99. The average for this metric is 99.985.", "is_anomalous": false}, {"value": 99.555, "average": 99.9705, "min_value": 99.800091554, "max_value": 100.169563618, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "9516c2eb056f690fe323db811a8a460c", "metric_id": "82e5869684b521040a0599a6ec8c5503", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "average", "anomaly_score": -4.19299955, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 99.555, "min_metric_value": 99.673218785, "max_metric_value": 100.267781215, "training_avg": 99.9705, "training_stddev": 0.09909373828, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last average value is 99.555. The average for this metric is 99.97.", "is_anomalous": true}], "result_description": "In column STANDARD_DEVIATION, the last average value is 99.555. The average for this metric is 99.97."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": "ZERO_PERCENT", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:24+02:00", "latest_run_time_utc": "2023-01-02T10:44:24+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "zero_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_zero_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('zero_count' as varchar)\n and upper(column_name) = upper(cast('ZERO_PERCENT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column ZERO_PERCENT, the last zero_count value is 122. The average for this metric is 42.5.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_zero_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('zero_count' as varchar)\n and upper(column_name) = upper(cast('ZERO_PERCENT' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Zero Count", "metrics": [{"value": 46.0, "average": 45.5, "min_value": 43.378679656, "max_value": 47.621320344, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "d5dbac3863b911859ee2111ff9313ff8", "metric_id": "1a4773c31e4ed1c70350a3869de0da04", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_count", "anomaly_score": 0.7071067812, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 46.0, "min_metric_value": 43.378679656, "max_metric_value": 47.621320344, "training_avg": 45.5, "training_stddev": 0.7071067812, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_count value is 46. The average for this metric is 45.5.", "is_anomalous": false}, {"value": 41.0, "average": 44.0, "min_value": 36.062746067, "max_value": 51.937253933, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "0eb7f2c2dcf99b04af13096af8ea3388", "metric_id": "0b411056494eb5bca9f8229f7d63bf01", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_count", "anomaly_score": -1.133893419, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 41.0, "min_metric_value": 36.062746067, "max_metric_value": 51.937253933, "training_avg": 44.0, "training_stddev": 2.645751311, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_count value is 41. The average for this metric is 44.", "is_anomalous": false}, {"value": 43.0, "average": 43.75, "min_value": 37.097932652, "max_value": 50.402067348, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "e9750c5d16efd9c33e758e1e77037d28", "metric_id": "e8d8b1665f771b7841464184511587ca", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_count", "anomaly_score": -0.3382407126, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 43.0, "min_metric_value": 37.097932652, "max_metric_value": 50.402067348, "training_avg": 43.75, "training_stddev": 2.217355783, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_count value is 43. The average for this metric is 43.75.", "is_anomalous": false}, {"value": 41.0, "average": 43.2, "min_value": 36.358947449, "max_value": 50.041052551, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "ce08292f37ea7ed21a10d67ca6d0dfb4", "metric_id": "9d1534dc05fa72c888d24d869a3e78e0", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_count", "anomaly_score": -0.9647638212, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 41.0, "min_metric_value": 36.358947449, "max_metric_value": 50.041052551, "training_avg": 43.2, "training_stddev": 2.28035085, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_count value is 41. The average for this metric is 43.2.", "is_anomalous": false}, {"value": 39.0, "average": 42.5, "min_value": 34.506252443, "max_value": 50.493747557, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "40f75b365059bb4d7d653631963398e0", "metric_id": "c4f820990b0df775f67037a98edff397", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_count", "anomaly_score": -1.313526594, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 39.0, "min_metric_value": 34.506252443, "max_metric_value": 50.493747557, "training_avg": 42.5, "training_stddev": 2.664582519, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_count value is 39. The average for this metric is 42.5.", "is_anomalous": false}, {"value": 43.0, "average": 42.571428571, "min_value": 35.252178024, "max_value": 49.890679119, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "970e99c6f8fd75f23da32e9d31569c4e", "metric_id": "591cf90d9d9a43020380bbc48d188e72", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_count", "anomaly_score": 0.1756620131, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 43.0, "min_metric_value": 35.252178024, "max_metric_value": 49.890679119, "training_avg": 42.571428571, "training_stddev": 2.439750182, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_count value is 43. The average for this metric is 42.571.", "is_anomalous": false}, {"value": 34.0, "average": 41.5, "min_value": 30.16106581, "max_value": 52.83893419, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "1783b0d64933304449f6de423622b865", "metric_id": "dd7f509a13f5b7a7197fee7b488458bc", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_count", "anomaly_score": -1.984313483, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 34.0, "min_metric_value": 30.16106581, "max_metric_value": 52.83893419, "training_avg": 41.5, "training_stddev": 3.77964473, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_count value is 34. The average for this metric is 41.5.", "is_anomalous": false}, {"value": 45.0, "average": 41.888888889, "min_value": 30.719734937, "max_value": 53.058042841, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "64639bb7d01ee23f3ff7a4347cfab5e7", "metric_id": "2abc49816e5b88b8f7f825b0d4ba69ae", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_count", "anomaly_score": 0.8356347646, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 45.0, "min_metric_value": 30.719734937, "max_metric_value": 53.058042841, "training_avg": 41.888888889, "training_stddev": 3.723051317, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_count value is 45. The average for this metric is 41.889.", "is_anomalous": false}, {"value": 44.0, "average": 42.1, "min_value": 31.380858243, "max_value": 52.819141757, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "e445d7a4894ee976b341a68e71a9c044", "metric_id": "b86fdabc638449ea75bfcc4a399c840c", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_count", "anomaly_score": 0.5317589905, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 44.0, "min_metric_value": 31.380858243, "max_metric_value": 52.819141757, "training_avg": 42.1, "training_stddev": 3.573047252, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_count value is 44. The average for this metric is 42.1.", "is_anomalous": false}, {"value": 38.0, "average": 41.727272727, "min_value": 30.90305745, "max_value": 52.551488004, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "b6936bea95af3ba2aa05c6e7de534847", "metric_id": "d4f304d2050a866b9c421b9eb7d18194", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_count", "anomaly_score": -1.033037305, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 38.0, "min_metric_value": 30.90305745, "max_metric_value": 52.551488004, "training_avg": 41.727272727, "training_stddev": 3.608071759, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_count value is 38. The average for this metric is 41.727.", "is_anomalous": false}, {"value": 45.0, "average": 42.0, "min_value": 31.29740898, "max_value": 52.70259102, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "345829a3ab3c20773a5d698c42c5011a", "metric_id": "b0830ffc667bdc79bd048d93c6745583", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_count", "anomaly_score": 0.8409178659, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 45.0, "min_metric_value": 31.29740898, "max_metric_value": 52.70259102, "training_avg": 42.0, "training_stddev": 3.56753034, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_count value is 45. The average for this metric is 42.", "is_anomalous": false}, {"value": 44.0, "average": 42.153846154, "min_value": 31.772650136, "max_value": 52.535042172, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "eb6d0d145609530b24b705c614e0e97e", "metric_id": "15848da2f7c986a384efab01e8b3ee51", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_count", "anomaly_score": 0.5335090031, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 44.0, "min_metric_value": 31.772650136, "max_metric_value": 52.535042172, "training_avg": 42.153846154, "training_stddev": 3.460398673, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_count value is 44. The average for this metric is 42.154.", "is_anomalous": false}, {"value": 41.0, "average": 42.071428571, "min_value": 32.054684348, "max_value": 52.088172795, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "f8d27579c892ee2c6ccd5e198886ccd7", "metric_id": "3d43fee627321a4a2b62885592c563aa", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_count", "anomaly_score": -0.3208912639, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 41.0, "min_metric_value": 32.054684348, "max_metric_value": 52.088172795, "training_avg": 42.071428571, "training_stddev": 3.338914741, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_count value is 41. The average for this metric is 42.071.", "is_anomalous": false}, {"value": 44.0, "average": 42.2, "min_value": 32.432707642, "max_value": 51.967292358, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "83b4ebcc8a96904b765dd09a8cbf15cc", "metric_id": "d79ff1573d6b6802a123140a8dda4d7b", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_count", "anomaly_score": 0.5528656052, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 44.0, "min_metric_value": 32.432707642, "max_metric_value": 51.967292358, "training_avg": 42.2, "training_stddev": 3.255764119, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_count value is 44. The average for this metric is 42.2.", "is_anomalous": false}, {"value": 42.0, "average": 42.1875, "min_value": 32.750206956, "max_value": 51.624793044, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "2d2e586f3e6ea5eaea3a11f6cca0e28c", "metric_id": "d6f2152284382a96f8d6a7bdba3a3f43", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_count", "anomaly_score": -0.05960395607, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 42.0, "min_metric_value": 32.750206956, "max_metric_value": 51.624793044, "training_avg": 42.1875, "training_stddev": 3.145764348, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_count value is 42. The average for this metric is 42.188.", "is_anomalous": false}, {"value": 35.0, "average": 41.764705882, "min_value": 31.236382738, "max_value": 52.293029027, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "e24e55a9377cbd84d0095d807a4d5b80", "metric_id": "b26609ee9a39a27ffa533917697b4609", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_count", "anomaly_score": -1.927573591, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 35.0, "min_metric_value": 31.236382738, "max_metric_value": 52.293029027, "training_avg": 41.764705882, "training_stddev": 3.509441048, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_count value is 35. The average for this metric is 41.765.", "is_anomalous": false}, {"value": 38.0, "average": 41.555555556, "min_value": 31.000378404, "max_value": 52.110732707, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "4b54859a31be547e29dd98043a66ddad", "metric_id": "d1e3c52c47e0bcd8d5b4993ca1b84a53", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_count", "anomaly_score": -1.010562543, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 38.0, "min_metric_value": 31.000378404, "max_metric_value": 52.110732707, "training_avg": 41.555555556, "training_stddev": 3.518392384, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_count value is 38. The average for this metric is 41.556.", "is_anomalous": false}, {"value": 29.0, "average": 40.894736842, "min_value": 27.482252477, "max_value": 54.307221207, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "2b503493c71e38db4e02294475e68f28", "metric_id": "a07688c336d32af0e4533bf98a476b94", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_count", "anomaly_score": -2.66052206, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 29.0, "min_metric_value": 27.482252477, "max_metric_value": 54.307221207, "training_avg": 40.894736842, "training_stddev": 4.470828122, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_count value is 29. The average for this metric is 40.895.", "is_anomalous": false}, {"value": 38.0, "average": 40.75, "min_value": 27.551614931, "max_value": 53.948385069, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "bbd88ae8e45235a74a541dc7fcfd2e8a", "metric_id": "b6d09e4def3df16d337ea636aa923285", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_count", "anomaly_score": -0.6250764739, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 38.0, "min_metric_value": 27.551614931, "max_metric_value": 53.948385069, "training_avg": 40.75, "training_stddev": 4.39946169, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_count value is 38. The average for this metric is 40.75.", "is_anomalous": false}, {"value": 29.0, "average": 40.19047619, "min_value": 25.201909119, "max_value": 55.179043262, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "8bd4551c182235a3d94a1b4f2567e959", "metric_id": "0ca98e6236e048e2f00c7c1e64c783ad", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_count", "anomaly_score": -2.239802405, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 29.0, "min_metric_value": 25.201909119, "max_metric_value": 55.179043262, "training_avg": 40.19047619, "training_stddev": 4.996189024, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_count value is 29. The average for this metric is 40.19.", "is_anomalous": false}, {"value": 41.0, "average": 40.227272727, "min_value": 25.590767928, "max_value": 54.863777526, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "e4667815f5f43ef6a5ca6144cf620b36", "metric_id": "7412873a8905cd50f7e92a8ac623ba21", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_count", "anomaly_score": 0.1583835656, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 41.0, "min_metric_value": 25.590767928, "max_metric_value": 54.863777526, "training_avg": 40.227272727, "training_stddev": 4.878834933, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_count value is 41. The average for this metric is 40.227.", "is_anomalous": false}, {"value": 32.0, "average": 39.869565217, "min_value": 24.671661264, "max_value": 55.067469171, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "c11a8d550ef9468f931500bfbe0a81c2", "metric_id": "facb793898f821c82f6ce91d7abae838", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_count", "anomaly_score": -1.553417874, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 32.0, "min_metric_value": 24.671661264, "max_metric_value": 55.067469171, "training_avg": 39.869565217, "training_stddev": 5.065967985, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_count value is 32. The average for this metric is 39.87.", "is_anomalous": false}, {"value": 37.0, "average": 39.75, "min_value": 24.782644217, "max_value": 54.717355783, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "5753ba37049143571df481f4d7148c6a", "metric_id": "32264bfe878a043aea4d0172761267e6", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_count", "anomaly_score": -0.5511995652, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 37.0, "min_metric_value": 24.782644217, "max_metric_value": 54.717355783, "training_avg": 39.75, "training_stddev": 4.989118594, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_count value is 37. The average for this metric is 39.75.", "is_anomalous": false}, {"value": 38.0, "average": 39.68, "min_value": 24.990207626, "max_value": 54.369792374, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "cb919e8da855453755c23573d946ccb7", "metric_id": "6baa64db75d8c9b0bb9f5d10f838fef9", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_count", "anomaly_score": -0.343095387, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 38.0, "min_metric_value": 24.990207626, "max_metric_value": 54.369792374, "training_avg": 39.68, "training_stddev": 4.896597458, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_count value is 38. The average for this metric is 39.68.", "is_anomalous": false}, {"value": 41.0, "average": 39.730769231, "min_value": 25.316833666, "max_value": 54.144704795, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "1420b3b63dcca14bb6104fe7658ccd68", "metric_id": "3a404ef3c3b0c0a6c9cdf9561f4a518d", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_count", "anomaly_score": 0.2641674295, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 41.0, "min_metric_value": 25.316833666, "max_metric_value": 54.144704795, "training_avg": 39.730769231, "training_stddev": 4.804645188, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_count value is 41. The average for this metric is 39.731.", "is_anomalous": false}, {"value": 46.0, "average": 39.962962963, "min_value": 25.37283634, "max_value": 54.553089586, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "2101aa4fc5bee0660ab60d55836459c6", "metric_id": "864ab421b365981b7aa1d4b84676fc2c", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_count", "anomaly_score": 1.24132652, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 46.0, "min_metric_value": 25.37283634, "max_metric_value": 54.553089586, "training_avg": 39.962962963, "training_stddev": 4.863375541, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_count value is 46. The average for this metric is 39.963.", "is_anomalous": false}, {"value": 29.0, "average": 39.571428571, "min_value": 23.963127971, "max_value": 55.179729171, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "17c952dceaf9ece4b5b85efe4dadbe9c", "metric_id": "16913a9654de0381a4e33f409afb2ed2", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_count", "anomaly_score": -2.031885887, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 29.0, "min_metric_value": 23.963127971, "max_metric_value": 55.179729171, "training_avg": 39.571428571, "training_stddev": 5.202766867, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_count value is 29. The average for this metric is 39.571.", "is_anomalous": false}, {"value": 45.0, "average": 39.75862069, "min_value": 24.136071621, "max_value": 55.381169759, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "b91b463eeaa79e58b0571025eb5843b4", "metric_id": "1ebbcb789072ec221964fcaeabf61571", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_count", "anomaly_score": 1.006502707, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 45.0, "min_metric_value": 24.136071621, "max_metric_value": 55.381169759, "training_avg": 39.75862069, "training_stddev": 5.207516356, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_count value is 45. The average for this metric is 39.759.", "is_anomalous": false}, {"value": 122.0, "average": 42.5, "min_value": 24.136071621, "max_value": 55.381169759, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "184e6af2e6a8912922bb7036530120cd", "metric_id": "fd5c387bcb31d66c321e5cf8f5c2cf75", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "zero_count", "anomaly_score": 5.011630871, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 122.0, "min_metric_value": -5.089299, "max_metric_value": 90.089299, "training_avg": 42.5, "training_stddev": 15.863099667, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last zero_count value is 122. The average for this metric is 42.5.", "is_anomalous": true}], "result_description": "In column ZERO_PERCENT, the last zero_count value is 122. The average for this metric is 42.5."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.singular_test_with_one_ref", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": null, "test_name": "singular_test_with_one_ref", "test_display_name": "Singular Test With One Ref", "latest_run_time": "2023-01-02T12:46:31+02:00", "latest_run_time_utc": "2023-01-02T10:46:31+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "dbt_test", "test_sub_type": "singular", "test_query": "select min from ELEMENTARY_TESTS.elon_test.numeric_column_anomalies where min < 100", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "Got 95 results, configured to fail if != 0", "result_query": "select min from ELEMENTARY_TESTS.elon_test.numeric_column_anomalies where min < 100"}, "configuration": {"test_name": "singular_test_with_one_ref", "test_params": null}}, "test_results": {"display_name": "singular_test_with_one_ref", "results_sample": [{"min": 65.0}, {"min": 97.0}, {"min": 26.0}, {"min": 88.0}, {"min": 31.0}], "error_message": "Got 95 results, configured to fail if != 0", "failed_rows_count": 95}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": "AVERAGE", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:22+02:00", "latest_run_time_utc": "2023-01-02T10:44:22+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "average", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_average__average__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('AVERAGE' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column AVERAGE, the last average value is 105.805. The average for this metric is 100.195.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_average__average__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('AVERAGE' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Average", "metrics": [{"value": 99.93, "average": 99.94, "min_value": 99.897573593, "max_value": 99.982426407, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "5e3a9339f52573d956db5e0336faf94a", "metric_id": "b90393972579520135bd0b24d1a5536b", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "detected_at": "2023-01-02T10:44:21.853000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "average", "anomaly_score": -0.7071067773, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 99.93, "min_metric_value": 99.897573593, "max_metric_value": 99.982426407, "training_avg": 99.94, "training_stddev": 0.0141421357, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last average value is 99.93. The average for this metric is 99.94.", "is_anomalous": false}, {"value": 99.99, "average": 99.956666667, "min_value": 99.865015153, "max_value": 100.04831818, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "aa6fecf23e9579b4d156c17d6ae9410e", "metric_id": "ec5c638881686b2fece4347f4f33fa8c", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "detected_at": "2023-01-02T10:44:21.853000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "average", "anomaly_score": 1.091089452, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 99.99, "min_metric_value": 99.865015153, "max_metric_value": 100.04831818, "training_avg": 99.956666667, "training_stddev": 0.0305505046, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last average value is 99.99. The average for this metric is 99.957.", "is_anomalous": false}, {"value": 100.035, "average": 99.97625, "min_value": 99.836943683, "max_value": 100.115556317, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "719709fef021547921a77ae8199f4da7", "metric_id": "09118a288b5c3054447a75af4b663453", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "detected_at": "2023-01-02T10:44:21.853000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "average", "anomaly_score": 1.265197469, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 100.035, "min_metric_value": 99.836943683, "max_metric_value": 100.115556317, "training_avg": 99.97625, "training_stddev": 0.04643543907, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last average value is 100.035. The average for this metric is 99.976.", "is_anomalous": false}, {"value": 100.085, "average": 99.998, "min_value": 99.808678844, "max_value": 100.187321156, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "c94c001a0149efd1751e56e2265207fb", "metric_id": "b1b3350d3081e1bca30daabe6ae107fe", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "detected_at": "2023-01-02T10:44:21.853000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "average", "anomaly_score": 1.378609796, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 100.085, "min_metric_value": 99.808678844, "max_metric_value": 100.187321156, "training_avg": 99.998, "training_stddev": 0.06310705193, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last average value is 100.085. The average for this metric is 99.998.", "is_anomalous": false}, {"value": 99.98, "average": 99.995, "min_value": 99.824237006, "max_value": 100.165762994, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "4363d87a00ac846101a2503008070691", "metric_id": "eae872e9b8fcc6bb749f7f3aa219678e", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "detected_at": "2023-01-02T10:44:21.853000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "average", "anomaly_score": -0.2635231381, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 99.98, "min_metric_value": 99.824237006, "max_metric_value": 100.165762994, "training_avg": 99.995, "training_stddev": 0.05692099794, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last average value is 99.98. The average for this metric is 99.995.", "is_anomalous": false}, {"value": 99.965, "average": 99.990714286, "min_value": 99.831161339, "max_value": 100.150267233, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "00ee30ea15b659bc066fd1f5e385b152", "metric_id": "ca42bdcfcf05c036ba34a47cff9cb341", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "detected_at": "2023-01-02T10:44:21.853000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "average", "anomaly_score": -0.4834937782, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 99.965, "min_metric_value": 99.831161339, "max_metric_value": 100.150267233, "training_avg": 99.990714286, "training_stddev": 0.05318431565, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last average value is 99.965. The average for this metric is 99.991.", "is_anomalous": false}, {"value": 100.055, "average": 99.99875, "min_value": 99.836055062, "max_value": 100.161444938, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "c4e5921fb79f8be34d2602042572209f", "metric_id": "d0b8a04c2240129f943c02c4b3b38588", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "detected_at": "2023-01-02T10:44:21.853000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "average", "anomaly_score": 1.037217273, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 100.055, "min_metric_value": 99.836055062, "max_metric_value": 100.161444938, "training_avg": 99.99875, "training_stddev": 0.054231646, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last average value is 100.055. The average for this metric is 99.999.", "is_anomalous": false}, {"value": 99.985, "average": 99.997222222, "min_value": 99.844415154, "max_value": 100.15002929, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "a7b51141e6651e6a79e8f32149a32002", "metric_id": "9f80bf13b7a559f497ac355ed5e82d22", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "detected_at": "2023-01-02T10:44:21.853000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "average", "anomaly_score": -0.2399539967, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 99.985, "min_metric_value": 99.844415154, "max_metric_value": 100.15002929, "training_avg": 99.997222222, "training_stddev": 0.0509356893, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last average value is 99.985. The average for this metric is 99.997.", "is_anomalous": false}, {"value": 99.975, "average": 99.995, "min_value": 99.849397802, "max_value": 100.140602198, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "7987a1debddb6265ff70b46d4cde5b31", "metric_id": "36e2b3fe932401d45c3e08bed960a666", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "detected_at": "2023-01-02T10:44:21.853000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "average", "anomaly_score": -0.4120816919, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 99.975, "min_metric_value": 99.849397802, "max_metric_value": 100.140602198, "training_avg": 99.995, "training_stddev": 0.04853406592, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last average value is 99.975. The average for this metric is 99.995.", "is_anomalous": false}, {"value": 100.015, "average": 99.996818182, "min_value": 99.857508194, "max_value": 100.13612817, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "27b6ce2b7a4d9f386a6471e2e1c572e0", "metric_id": "1adffa3f3de1ad4590dedea4cb3fb5b7", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "detected_at": "2023-01-02T10:44:21.853000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "average", "anomaly_score": 0.3915401574, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 100.015, "min_metric_value": 99.857508194, "max_metric_value": 100.13612817, "training_avg": 99.996818182, "training_stddev": 0.04643666259, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last average value is 100.015. The average for this metric is 99.997.", "is_anomalous": false}, {"value": 100.075, "average": 100.003333333, "min_value": 99.854245196, "max_value": 100.152421471, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "34f51ac95f9ef2be7f8ffcba8f65d64d", "metric_id": "0754875aff5ed0175ff6eeb7dbb2bcd6", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "detected_at": "2023-01-02T10:44:21.853000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "average", "anomaly_score": 1.442099981, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 100.075, "min_metric_value": 99.854245196, "max_metric_value": 100.152421471, "training_avg": 100.003333333, "training_stddev": 0.04969604577, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last average value is 100.075. The average for this metric is 100.003.", "is_anomalous": false}, {"value": 100.035, "average": 100.005769231, "min_value": 99.860616791, "max_value": 100.15092167, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "e8806eba190ca6089255523a59a3745a", "metric_id": "577314c4a800c104bb60de8610d9bb00", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "detected_at": "2023-01-02T10:44:21.853000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "average", "anomaly_score": 0.6041393993, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 100.035, "min_metric_value": 99.860616791, "max_metric_value": 100.15092167, "training_avg": 100.005769231, "training_stddev": 0.04838414655, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last average value is 100.035. The average for this metric is 100.006.", "is_anomalous": false}, {"value": 100.005, "average": 100.005714286, "min_value": 99.866254969, "max_value": 100.145173603, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "d3314a09ac005f7d0ed79ca84215f590", "metric_id": "fa8f6c0d5a26f81e46d9208129100e8b", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "detected_at": "2023-01-02T10:44:21.853000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "average", "anomaly_score": -0.01536546422, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 100.005, "min_metric_value": 99.866254969, "max_metric_value": 100.145173603, "training_avg": 100.005714286, "training_stddev": 0.04648643894, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last average value is 100.005. The average for this metric is 100.006.", "is_anomalous": false}, {"value": 99.97, "average": 100.003333333, "min_value": 99.866129106, "max_value": 100.14053756, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "51a4f41cc3523ba9ac8d1f540c3f6fca", "metric_id": "066e42c956cc19a63251f5e4d791e881", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "detected_at": "2023-01-02T10:44:21.853000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "average", "anomaly_score": -0.7288405184, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 99.97, "min_metric_value": 99.866129106, "max_metric_value": 100.14053756, "training_avg": 100.003333333, "training_stddev": 0.04573474236, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last average value is 99.97. The average for this metric is 100.003.", "is_anomalous": false}, {"value": 100.035, "average": 100.0053125, "min_value": 99.870649727, "max_value": 100.139975273, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "7154662f086754a5ab2acece263d0e1d", "metric_id": "d6846df98dc9be3c81a101400d6caa0f", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "detected_at": "2023-01-02T10:44:21.853000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "average", "anomaly_score": 0.6613743206, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 100.035, "min_metric_value": 99.870649727, "max_metric_value": 100.139975273, "training_avg": 100.0053125, "training_stddev": 0.044887591, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last average value is 100.035. The average for this metric is 100.005.", "is_anomalous": false}, {"value": 100.015, "average": 100.005882353, "min_value": 99.875305297, "max_value": 100.136459409, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "3672a1be82e62f347fe325bc556aae4b", "metric_id": "815af066c0dbe9e24f329f65abd3d7cf", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "detected_at": "2023-01-02T10:44:21.853000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "average", "anomaly_score": 0.2094773921, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 100.015, "min_metric_value": 99.875305297, "max_metric_value": 100.136459409, "training_avg": 100.005882353, "training_stddev": 0.04352568536, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last average value is 100.015. The average for this metric is 100.006.", "is_anomalous": false}, {"value": 99.845, "average": 99.996944444, "min_value": 99.826682968, "max_value": 100.167205921, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "3d1200a14f5e3a20ece8fdf9eafa7154", "metric_id": "8c565848b1a70bd6560ec3285f0cd3c2", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "detected_at": "2023-01-02T10:44:21.853000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "average", "anomaly_score": -2.677254665, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 99.845, "min_metric_value": 99.826682968, "max_metric_value": 100.167205921, "training_avg": 99.996944444, "training_stddev": 0.05675382563, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last average value is 99.845. The average for this metric is 99.997.", "is_anomalous": false}, {"value": 100.055, "average": 100.0, "min_value": 99.829779555, "max_value": 100.170220445, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "d7aa4a01089e57c89abb41f66bb352df", "metric_id": "5a9ff397c3fc8a95cfc68569d97140c4", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "detected_at": "2023-01-02T10:44:21.853000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "average", "anomaly_score": 0.9693312688, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 100.055, "min_metric_value": 99.829779555, "max_metric_value": 100.170220445, "training_avg": 100.0, "training_stddev": 0.05674014836, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last average value is 100.055. The average for this metric is 100.", "is_anomalous": false}, {"value": 99.975, "average": 99.99875, "min_value": 99.832222975, "max_value": 100.165277025, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "bf8098a8142581190647b430e8e13f59", "metric_id": "bcce697221e2786a04600cbcbccb5618", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "detected_at": "2023-01-02T10:44:21.853000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "average", "anomaly_score": -0.4278584822, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 99.975, "min_metric_value": 99.832222975, "max_metric_value": 100.165277025, "training_avg": 99.99875, "training_stddev": 0.05550900821, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last average value is 99.975. The average for this metric is 99.999.", "is_anomalous": false}, {"value": 100.015, "average": 99.99952381, "min_value": 99.836865096, "max_value": 100.162182523, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "9edde8ce857889b0206dac5d6c610e5f", "metric_id": "dd999b7867a01e026a0d5505118a9963", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "detected_at": "2023-01-02T10:44:21.853000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "average", "anomaly_score": 0.2854355013, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 100.015, "min_metric_value": 99.836865096, "max_metric_value": 100.162182523, "training_avg": 99.99952381, "training_stddev": 0.05421957117, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last average value is 100.015. The average for this metric is 100.", "is_anomalous": false}, {"value": 99.935, "average": 99.996590909, "min_value": 99.83257525, "max_value": 100.160606568, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "5240326de6a058b0119ef41a94e54931", "metric_id": "2eabfb6c4f933ca0c1c6e6ff172320cc", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "detected_at": "2023-01-02T10:44:21.853000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "average", "anomaly_score": -1.126555408, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 99.935, "min_metric_value": 99.83257525, "max_metric_value": 100.160606568, "training_avg": 99.996590909, "training_stddev": 0.05467188624, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last average value is 99.935. The average for this metric is 99.997.", "is_anomalous": false}, {"value": 100.005, "average": 99.996956522, "min_value": 99.836625528, "max_value": 100.157287516, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "715dde4789d5ad08cb122bef83c55f8a", "metric_id": "5370444eb66d74208cee5122b37f843c", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "detected_at": "2023-01-02T10:44:21.853000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "average", "anomaly_score": 0.1505038683, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 100.005, "min_metric_value": 99.836625528, "max_metric_value": 100.157287516, "training_avg": 99.996956522, "training_stddev": 0.05344366461, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last average value is 100.005. The average for this metric is 99.997.", "is_anomalous": false}, {"value": 100.03, "average": 99.998333333, "min_value": 99.840226325, "max_value": 100.156440341, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "282f078b4f96bafa2a30afc481f82d52", "metric_id": "bf5ce91fc06261a3020ac988cb09ad98", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "detected_at": "2023-01-02T10:44:21.853000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "average", "anomaly_score": 0.6008588813, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 100.03, "min_metric_value": 99.840226325, "max_metric_value": 100.156440341, "training_avg": 99.998333333, "training_stddev": 0.05270233603, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last average value is 100.03. The average for this metric is 99.998.", "is_anomalous": false}, {"value": 100.075, "average": 100.0014, "min_value": 99.839930963, "max_value": 100.162869037, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "9ca0a25a9f11eb373d94a201dbbff542", "metric_id": "09b9b738df18624a2cf693ddc002c0a4", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "detected_at": "2023-01-02T10:44:21.853000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "average", "anomaly_score": 1.367444831, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 100.075, "min_metric_value": 99.839930963, "max_metric_value": 100.162869037, "training_avg": 100.0014, "training_stddev": 0.05382301235, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last average value is 100.075. The average for this metric is 100.001.", "is_anomalous": false}, {"value": 99.98, "average": 100.000576923, "min_value": 99.841870009, "max_value": 100.159283837, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "40caab3f04402b03a34d2e122c34ce82", "metric_id": "8c0dd493c822c0df266b29c2cbbb97df", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "detected_at": "2023-01-02T10:44:21.853000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "average", "anomaly_score": -0.3889608063, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 99.98, "min_metric_value": 99.841870009, "max_metric_value": 100.159283837, "training_avg": 100.000576923, "training_stddev": 0.05290230466, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last average value is 99.98. The average for this metric is 100.001.", "is_anomalous": false}, {"value": 100.015, "average": 100.001111111, "min_value": 99.845263553, "max_value": 100.15695867, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "23b58f1ac1d660bb171f3c0982a6efc3", "metric_id": "1d9e2209cef83c8ea4c71f0f0dd18860", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "detected_at": "2023-01-02T10:44:21.853000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "average", "anomaly_score": 0.2673552738, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 100.015, "min_metric_value": 99.845263553, "max_metric_value": 100.15695867, "training_avg": 100.001111111, "training_stddev": 0.05194918616, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last average value is 100.015. The average for this metric is 100.001.", "is_anomalous": false}, {"value": 100.0, "average": 100.001071429, "min_value": 99.848135868, "max_value": 100.154006989, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "328a0e82d6c89c204f43639b4e141d76", "metric_id": "9cbfcf1193dbfafba10e77ec9423fbe9", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "detected_at": "2023-01-02T10:44:21.853000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "average", "anomaly_score": -0.02101725527, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 100.0, "min_metric_value": 99.848135868, "max_metric_value": 100.154006989, "training_avg": 100.001071429, "training_stddev": 0.05097852015, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last average value is 100. The average for this metric is 100.001.", "is_anomalous": false}, {"value": 100.02, "average": 100.001724138, "min_value": 99.851174652, "max_value": 100.152273624, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "079207b7e88d62e879c338f14b8b293b", "metric_id": "2afb720bc3bd3613574fdbb40efb2318", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "detected_at": "2023-01-02T10:44:21.853000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "average", "anomaly_score": 0.364183151, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 100.02, "min_metric_value": 99.851174652, "max_metric_value": 100.152273624, "training_avg": 100.001724138, "training_stddev": 0.05018316201, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last average value is 100.02. The average for this metric is 100.002.", "is_anomalous": false}, {"value": 105.805, "average": 100.195166667, "min_value": 99.851174652, "max_value": 100.152273624, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "512f78ff7fc194cb5432dfff891f4332", "metric_id": "50c72e986af699cea6feaf21840187e9", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "detected_at": "2023-01-02T10:44:21.853000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "average", "anomaly_score": 5.288926679, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 105.805, "min_metric_value": 97.013141084, "max_metric_value": 103.37719225, "training_avg": 100.195166667, "training_stddev": 1.060675194, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last average value is 105.805. The average for this metric is 100.195.", "is_anomalous": true}], "result_description": "In column AVERAGE, the last average value is 105.805. The average for this metric is 100.195."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": "STANDARD_DEVIATION", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:25+02:00", "latest_run_time_utc": "2023-01-02T10:44:25+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "max", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_standard_deviation__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('max' as varchar)\n and upper(column_name) = upper(cast('STANDARD_DEVIATION' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column STANDARD_DEVIATION, the last max value is 120. The average for this metric is 101.633.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_standard_deviation__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('max' as varchar)\n and upper(column_name) = upper(cast('STANDARD_DEVIATION' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Max", "metrics": [{"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "d641ed7aa8e037e56d063aff5110abd1", "metric_id": "a3a780f65358b022aacddb04973d29e6", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "f3dfb188df09946bf5125fd2ac5d1df2", "metric_id": "c46db58b64256ceea173769bbf853c62", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "ccb03fabd32e2d0ed6950fd28781a881", "metric_id": "0fdab4f7e4ebbffc5871672a4aa04268", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "47d0f142acc2773de3796a683bb2f885", "metric_id": "baad01269e3d3ca6833b9134af5d3dd3", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "c864662be3a7378a3ca1d51e3876cce5", "metric_id": "6b8cf66bd812edce37d7f130d320fc08", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "837bf517335ebab083c90ac1e1884837", "metric_id": "4641e6174cc4147a76348bc3d423999c", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "1d7914df072786789d2fd02906c84727", "metric_id": "783f8354dc9bb65894c57f74b371c072", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "d01f36704c47476e02e0029e636f5d26", "metric_id": "4f24c9bcc914317802952ff783ff36e1", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "e89b540af691dcdcd5286fbfce8b439d", "metric_id": "51b911bf69fee36b261021219984b6bd", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "e0444999932a749e4ea725e3fe94bfab", "metric_id": "4848ddc6a020c8783da228e9abbe0462", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "6c552bea639b4ef2cc13a22b55b4d317", "metric_id": "bef0d0c029b737e78c77bef803d713b9", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "c5b79723eefcd9bb5a428335c55645f7", "metric_id": "3d76247aea66366f1dfba58ebe022404", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "fdf58e9be3eefaf4be7ad0d60c937b10", "metric_id": "c0557599ebdb9a9dd21924a6a79a9307", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "f6474e96c8a4ea7922260322f8e78a06", "metric_id": "7fcb195d586487b9c2f46b775f9a3494", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "ae955c38b50e600da480b86721bf6b62", "metric_id": "5817f22780b28a7d86a90e4eef68c6c7", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "eb0df91887dfb7523eafe4695dddf6ae", "metric_id": "2c92140a8f68f0f213aa8e62680f2822", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "cfe76d576086b3599da67a2385d43690", "metric_id": "c39de56cdb6b48bfc29cde9a37ff3134", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "d24d43c568ce926739afd1072a2ad3c6", "metric_id": "e83111c5870a7312a58cf744cef1e56d", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "7581d9bf5ec9283d53f7a7d9e28fe3bc", "metric_id": "cf826afc0c11e9583a3ce8cb6c61bf80", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "39008fb71def00112e9c6b9f32152943", "metric_id": "b5da4c276bf6cafd108b26f84959f1d3", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "2614e6d0839175b322b2ec182de32593", "metric_id": "b51a73174bdd555a15bb808ddfdf1f07", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "2583b95c0e23ae9f1b7ca8045c164648", "metric_id": "3bc0175af737ae0cd48d734b7fe39ba1", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "8ed4e0b72d8bc4110690d3828bc0a6ff", "metric_id": "047d3983468e944cc40a34dc5ec5e97d", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "2f0c30efea5e71e8df45f5d45defa260", "metric_id": "27d700be3af2ca7d059ee679d7da439f", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "fa6968483123c25cc4f1df891eecac79", "metric_id": "28f691ddc0b4c832115e8222c1f9843f", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "6c35444c38d9bee6679a171640266f6c", "metric_id": "ac80418512b60c04f6b2125f59c13b2d", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "b9706ec85ea7d16a4b768740a3590690", "metric_id": "e9b1e9c36e723ca93609665d519abfbf", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "cb058abaa13467924d66fe69bf78a640", "metric_id": "79486631a0f39fc4c916230806faec4d", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 120.0, "average": 101.633333333, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "53d1bd40aed7ccac6974ee59d1a416fa", "metric_id": "5a7e5af4e510b58fb847948af8367a15", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "max", "anomaly_score": 5.294651389, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 120.0, "min_metric_value": 91.226604741, "max_metric_value": 112.040061926, "training_avg": 101.633333333, "training_stddev": 3.468909531, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last max value is 120. The average for this metric is 101.633.", "is_anomalous": true}], "result_description": "In column STANDARD_DEVIATION, the last max value is 120. The average for this metric is 101.633."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": "STANDARD_DEVIATION", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:25+02:00", "latest_run_time_utc": "2023-01-02T10:44:25+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_standard_deviation__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('STANDARD_DEVIATION' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column STANDARD_DEVIATION, the last null_percent value is 0. The average for this metric is 0.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_standard_deviation__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('STANDARD_DEVIATION' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Null Percent", "metrics": [{"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "5ce6d2b2b9c427a48468f404a2686481", "metric_id": "88c7a6c4d12493314ce3c3942d79f46e", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "5e6f7461ad96650579c1ce7bb21f9749", "metric_id": "2fdc64e40e81a8e2a18c57b2a3562fd4", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "6c04a55699e1508747424aae96d74d5a", "metric_id": "aa534c501c8589121660ee78d24c5055", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "168c3efeaa35cde8502613d5b5609f0b", "metric_id": "08e0a781f3bf5782cf7c1e7d1770695b", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "616b87c20f512c578ce4ee77f40d7dcb", "metric_id": "dcf8a25f76b05134677a9f3ae1cc981b", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "5f6bac61aceca0c171f9735323a57b9c", "metric_id": "8732f6d484523863e29995fb7babc3f3", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "b90963d53c8235b6a4b45c66596fcb32", "metric_id": "450713bccf16f6ad0dc3624e33affd0a", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "96814a6dce3c2ec284c7f284fa174fc3", "metric_id": "fa407ff9b956c939618ca5eb661a65da", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "517edfa62353f0dedd4fcde343171746", "metric_id": "7ce22f1d84f574c8156396dc1cf20447", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "249dff73179baa0a9af881142eaeddd9", "metric_id": "4ae28835d4bdf276a8516ad661cd8b10", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "53ab3089dc1160a08cec517be777171a", "metric_id": "82dee23e57b8a288afd5d6c2ba3595dd", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "2de33f9772dd0395ede700e62d77809f", "metric_id": "3c17d6f3afd7fcd8c6906dfcaaf55a1c", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "2b7c51d6b9880382fa5afbb3d882a4a3", "metric_id": "085728c4411c745f752a74a70254d96c", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "d1489bc88b5eb3f8165784676a0fecb6", "metric_id": "b80de9a0f032ce23ee0dd07a0e91db91", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "a0d20113ac2953ffde8b5d4d422a377a", "metric_id": "79eae97350f73250b7fef20b2a9b64e5", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "b4b9b1cf6ca6cd2f4ea716ba50e45287", "metric_id": "bc69c2006dc0b2e19ff54bdb4cbced76", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "3d41799177620c706e72a630a00a51dd", "metric_id": "8f21963f4577a6d15bc6740cf8292bb0", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "f98d3e05806922901decbb9f665326d6", "metric_id": "b9708a3c788d78dacea57aeff01d5680", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "16f0d6819475577e176075f5765d517e", "metric_id": "b90cf7abbe9ca742a89b064f10d973e5", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "fe9761210060549d7f516c9361b7c8ea", "metric_id": "2bec8c7651189d8a1fcbd77689b7b9c4", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "4b2ea498b724df58d9d58a0f27f79f6b", "metric_id": "8c9d1dd2a9c2b9a350917889eb742579", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "5c519e19bb8679efa248278b95b8f7ff", "metric_id": "055528ef42d8a83abcdd4644cefbcfe6", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "33c1b916a95256ed436807913b2832a5", "metric_id": "2388e821f76682a32c2db8f854453e71", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "d6e477f15217da644ac068fde6f2eeb2", "metric_id": "5be234bfd2385a52c5caca3fb5665dd4", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "2c58d17853ad5f6c06a8700359212446", "metric_id": "955caae4aa0d2e3800990beb1b860b5e", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "6ab4d5060b7ff335dbb3e5606069ae15", "metric_id": "470768bce4abbafc3bec509c8448ebe3", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "f6408e4f7dee3eb0d411e249a344b30d", "metric_id": "7db280a70761059fca0690f4a8bace54", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "ade3bf6a38cc4c754cad8448dc7bd007", "metric_id": "09cab5c5940fb2d616c9299f6b00ba77", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "21198867b7f6466cccddfe57f201de75", "metric_id": "d0eb93bdac27b42beea9b89840b7e114", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}], "result_description": "In column STANDARD_DEVIATION, the last null_percent value is 0. The average for this metric is 0."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": "STANDARD_DEVIATION", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:25+02:00", "latest_run_time_utc": "2023-01-02T10:44:25+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "min", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_standard_deviation__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('min' as varchar)\n and upper(column_name) = upper(cast('STANDARD_DEVIATION' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column STANDARD_DEVIATION, the last min value is 80. The average for this metric is 98.367.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_standard_deviation__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('min' as varchar)\n and upper(column_name) = upper(cast('STANDARD_DEVIATION' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Min", "metrics": [{"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "a3af8dac717820923d592e812bfbf504", "metric_id": "2b35f1b2b08460a8e9a007bdf2c5ac72", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "f24c83d99a72d6ad1425db7ebcb3fd7a", "metric_id": "fd7828f6724e375613e19697d7b9971f", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "b301622144c88ae575ff21ea8f03ac16", "metric_id": "0e9e2c6a685b000877bba375cc1dbb69", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "8064cf7e74f73d357b6cea4d57a03d4b", "metric_id": "bed685c8c0d5fdb7841778800c53b882", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "efe6e138875633bd392b0a7d410ef862", "metric_id": "3ff80dfad891cc14f798ce0ca1cd417b", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "42e4cb4c9f826318ded69d0acda16a91", "metric_id": "81960e265c862f9aeceea135cf8b8e3f", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "f76e8b771d24fa4799bba18c41c1d218", "metric_id": "7cde8c6de7a6a0eb4b6e0e432df1fe65", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "0e30cf2050dcab6793643cca7762a01d", "metric_id": "f5a740de62f0756c21694c6b5ede19cb", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "373c35cf62ce9f2fa96ead143c6d8a09", "metric_id": "0c1b8d9b54ab1dc219f6da7e8a457f1f", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "84b4ebe75efda21df3e393f6f106dcf4", "metric_id": "bae50c8d54737619a824c959d30c2d20", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "a3a0c2e3dbbb85c7ebbfaf56df082d10", "metric_id": "4932e679598fd69d4d2ecf44fc835fa1", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "265e0a4f988b5d231908785e35fa53e1", "metric_id": "4448bb1a5f7eec7805a46b239645e820", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "5a910dc4d35aab52de4c8103b5bf2c0e", "metric_id": "9661f060a1a7f8a0034483045a986841", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "bf6268acb9b3543d1761889e6e78703e", "metric_id": "ec0ef877e4c0660f7774c4796a0f571d", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "8a38d9160bf221c5e3a2040cbfe4eaa1", "metric_id": "456402df0ec62f897986003bc16b247f", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "ffe8a642cff22221755cf97aa53e48c2", "metric_id": "ae48c1cef0c04b5a82e6f8235b4e8eb1", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "25e9fa073788aa66dd8148130a68bc01", "metric_id": "def1dca10d9851d5bb106864cf0f5675", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "7b11fe6f8ae5ede858af79ef593195ca", "metric_id": "85a5bd46f532c06394eac729e71ed1f6", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "bee4843b0022d80a73552a3f7739831c", "metric_id": "10e1319db88d49822044a61c90cc08eb", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "3edf2c5ee5401758e250cf98c2aeeed2", "metric_id": "40c2087a817d647bd2355e49b0dfec52", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "5af27262d2cac0399fbe954da862ef9e", "metric_id": "ff38d9a9a983478cf5221d30785eb370", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "7a7f7a9ce2fa6cf2bc1fdb5e90aef89d", "metric_id": "d4c942ae1f6716481311319439292f0d", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "5d757bd9e8eba8eb510ff8092417af53", "metric_id": "5d7b9fc5451198aa60a652b698aa06da", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "e645de0323299f6863b20068499c9a21", "metric_id": "78fa6f29bdeaf77d650b38c16b13f62e", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "6af2a01b28fea45adea7b221bef91c7d", "metric_id": "6bf8f15b317204b0bde1a04372f9aa33", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "984879228b23f25f4551d616055a16b9", "metric_id": "4ecf35793d214c3fa7b64af813e51be7", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "c66fbb05583eebadad48ee14b1397855", "metric_id": "13143ee4745303db8fb83fa422367897", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 99.0, "average": 99.0, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "aed16d8710852b559830ab55f5a8bfa2", "metric_id": "6b43dcceff36024cbf92955a0eec6ab0", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 99.0, "min_metric_value": 99.0, "max_metric_value": 99.0, "training_avg": 99.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last min value is 99. The average for this metric is 99.", "is_anomalous": false}, {"value": 80.0, "average": 98.366666667, "min_value": 99.0, "max_value": 99.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "a5e59a215768bf482aa9063bcdc853be", "metric_id": "f651dfc22238582e2bc56cb24e3f8ee3", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "min", "anomaly_score": -5.294651389, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 80.0, "min_metric_value": 87.959938074, "max_metric_value": 108.773395259, "training_avg": 98.366666667, "training_stddev": 3.468909531, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last min value is 80. The average for this metric is 98.367.", "is_anomalous": true}], "result_description": "In column STANDARD_DEVIATION, the last min value is 80. The average for this metric is 98.367."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": "MIN", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:22+02:00", "latest_run_time_utc": "2023-01-02T10:44:22+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "average", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_average__min__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('MIN' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column MIN, the last average value is 102.795. The average for this metric is 148.295.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_average__min__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('MIN' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Average", "metrics": [{"value": 146.33, "average": 148.2575, "min_value": 140.079810076, "max_value": 156.435189924, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "2a3d584d6cc9be7dba628b0c38636ed5", "metric_id": "995beda05acc46f51acc48fd9169981f", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "detected_at": "2023-01-02T10:44:21.823000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MIN", "metric_name": "average", "anomaly_score": -0.7071067812, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 146.33, "min_metric_value": 140.079810076, "max_metric_value": 156.435189924, "training_avg": 148.2575, "training_stddev": 2.725896641, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN, the last average value is 146.33. The average for this metric is 148.258.", "is_anomalous": false}, {"value": 151.225, "average": 149.246666667, "min_value": 141.510034648, "max_value": 156.983298686, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "87bd6f206335d268fd122851fa687f05", "metric_id": "337a0060288a2b312774b21098a90894", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "detected_at": "2023-01-02T10:44:21.823000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MIN", "metric_name": "average", "anomaly_score": 0.7671296742, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 151.225, "min_metric_value": 141.510034648, "max_metric_value": 156.983298686, "training_avg": 149.246666667, "training_stddev": 2.57887734, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN, the last average value is 151.225. The average for this metric is 149.247.", "is_anomalous": false}, {"value": 146.91, "average": 148.6625, "min_value": 141.438327037, "max_value": 155.886672963, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "7a2aba09dbac5c954889bbb93085daaf", "metric_id": "003a40a503f84b0b6f100f7d78f39ce9", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "detected_at": "2023-01-02T10:44:21.823000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MIN", "metric_name": "average", "anomaly_score": -0.7277649673, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 146.91, "min_metric_value": 141.438327037, "max_metric_value": 155.886672963, "training_avg": 148.6625, "training_stddev": 2.408057654, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN, the last average value is 146.91. The average for this metric is 148.663.", "is_anomalous": false}, {"value": 151.3, "average": 149.19, "min_value": 142.002299568, "max_value": 156.377700432, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "3339cc22d610d00696e7423efba1b3dc", "metric_id": "990edf0e4cecd3b7d46feab6b2196fef", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "detected_at": "2023-01-02T10:44:21.823000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MIN", "metric_name": "average", "anomaly_score": 0.8806710936, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 151.3, "min_metric_value": 142.002299568, "max_metric_value": 156.377700432, "training_avg": 149.19, "training_stddev": 2.395900144, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN, the last average value is 151.3. The average for this metric is 149.19.", "is_anomalous": false}, {"value": 150.545, "average": 149.415833333, "min_value": 142.776219828, "max_value": 156.055446839, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "0fa85c273e1016955e32d6778cee8de4", "metric_id": "04edc343601a4a6e6e7c421545f63cdb", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "detected_at": "2023-01-02T10:44:21.823000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MIN", "metric_name": "average", "anomaly_score": 0.5101953596, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 150.545, "min_metric_value": 142.776219828, "max_metric_value": 156.055446839, "training_avg": 149.415833333, "training_stddev": 2.213204502, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN, the last average value is 150.545. The average for this metric is 149.416.", "is_anomalous": false}, {"value": 150.24, "average": 149.533571429, "min_value": 143.400841321, "max_value": 155.666301536, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "9cb40abcf8664da627d00fefc7a3e9db", "metric_id": "0983d373d668b38ed9ac708e22185e00", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "detected_at": "2023-01-02T10:44:21.823000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MIN", "metric_name": "average", "anomaly_score": 0.3455697018, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 150.24, "min_metric_value": 143.400841321, "max_metric_value": 155.666301536, "training_avg": 149.533571429, "training_stddev": 2.044243369, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN, the last average value is 150.24. The average for this metric is 149.534.", "is_anomalous": false}, {"value": 150.025, "average": 149.595, "min_value": 143.893319797, "max_value": 155.296680203, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "4c3d862d6d880d3468b09e47970efcc2", "metric_id": "f2931094a0afe29232042e48673646f4", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "detected_at": "2023-01-02T10:44:21.823000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MIN", "metric_name": "average", "anomaly_score": 0.2262490975, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 150.025, "min_metric_value": 143.893319797, "max_metric_value": 155.296680203, "training_avg": 149.595, "training_stddev": 1.900560068, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN, the last average value is 150.025. The average for this metric is 149.595.", "is_anomalous": false}, {"value": 152.21, "average": 149.885555556, "min_value": 143.945543981, "max_value": 155.82556713, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "6ba4a0db2235c43e1ed2e223e66d90d1", "metric_id": "05802347962de97ecf9cc11a26ebcb94", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "detected_at": "2023-01-02T10:44:21.823000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MIN", "metric_name": "average", "anomaly_score": 1.173959553, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 152.21, "min_metric_value": 143.945543981, "max_metric_value": 155.82556713, "training_avg": 149.885555556, "training_stddev": 1.980003858, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN, the last average value is 152.21. The average for this metric is 149.886.", "is_anomalous": false}, {"value": 148.515, "average": 149.7485, "min_value": 143.999247657, "max_value": 155.497752343, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "0bc3c048b247169cf111a0d68795e8ff", "metric_id": "406f1aa2b42fc9a09eeb342abf6bef04", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "detected_at": "2023-01-02T10:44:21.823000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MIN", "metric_name": "average", "anomaly_score": -0.6436489094, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 148.515, "min_metric_value": 143.999247657, "max_metric_value": 155.497752343, "training_avg": 149.7485, "training_stddev": 1.916417448, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN, the last average value is 148.515. The average for this metric is 149.749.", "is_anomalous": false}, {"value": 149.21, "average": 149.699545455, "min_value": 144.223619007, "max_value": 155.175471902, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "85cbf3d52898392e85bdbcf0c6b133c8", "metric_id": "67c11a8d72c044c211f5ae2d8f0634e1", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "detected_at": "2023-01-02T10:44:21.823000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MIN", "metric_name": "average", "anomaly_score": -0.2681987017, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 149.21, "min_metric_value": 144.223619007, "max_metric_value": 155.175471902, "training_avg": 149.699545455, "training_stddev": 1.825308816, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN, the last average value is 149.21. The average for this metric is 149.7.", "is_anomalous": false}, {"value": 150.415, "average": 149.759166667, "min_value": 144.501439237, "max_value": 155.016894097, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "4279b29a44933ab1597de90c83616501", "metric_id": "4665bf9f7ac4f5e344e0406b1fa1a4e6", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "detected_at": "2023-01-02T10:44:21.823000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MIN", "metric_name": "average", "anomaly_score": 0.3742111066, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 150.415, "min_metric_value": 144.501439237, "max_metric_value": 155.016894097, "training_avg": 149.759166667, "training_stddev": 1.75257581, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN, the last average value is 150.415. The average for this metric is 149.759.", "is_anomalous": false}, {"value": 148.075, "average": 149.629615385, "min_value": 144.404318282, "max_value": 154.854912487, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "7dacb42bc7d0815d74a4627bf9a2ed14", "metric_id": "b026bb824579ac159e6340b2fcaf7010", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "detected_at": "2023-01-02T10:44:21.823000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MIN", "metric_name": "average", "anomaly_score": -0.8925513827, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 148.075, "min_metric_value": 144.404318282, "max_metric_value": 154.854912487, "training_avg": 149.629615385, "training_stddev": 1.741765701, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN, the last average value is 148.075. The average for this metric is 149.63.", "is_anomalous": false}, {"value": 152.695, "average": 149.848571429, "min_value": 144.258928927, "max_value": 155.43821393, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "2ec6ef9540d99c3128a663af0e3a280a", "metric_id": "94e0d5f692a152d2823c290b8ca7e57f", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "detected_at": "2023-01-02T10:44:21.823000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MIN", "metric_name": "average", "anomaly_score": 1.527698008, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 152.695, "min_metric_value": 144.258928927, "max_metric_value": 155.43821393, "training_avg": 149.848571429, "training_stddev": 1.863214167, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN, the last average value is 152.695. The average for this metric is 149.849.", "is_anomalous": false}, {"value": 151.175, "average": 149.937, "min_value": 144.453567746, "max_value": 155.420432254, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "f1e0a26af82a1c88abe9a7c363e16eeb", "metric_id": "0e858b861fda706631f28a68766ecd4a", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "detected_at": "2023-01-02T10:44:21.823000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MIN", "metric_name": "average", "anomaly_score": 0.6773130091, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 151.175, "min_metric_value": 144.453567746, "max_metric_value": 155.420432254, "training_avg": 149.937, "training_stddev": 1.827810751, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN, the last average value is 151.175. The average for this metric is 149.937.", "is_anomalous": false}, {"value": 148.325, "average": 149.83625, "min_value": 144.402542702, "max_value": 155.269957298, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "60b389226cb6966ed3685314eb86a019", "metric_id": "e6ddf0e506fc62a27cdd5fef9c9f96fb", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "detected_at": "2023-01-02T10:44:21.823000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MIN", "metric_name": "average", "anomaly_score": -0.8343750871, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 148.325, "min_metric_value": 144.402542702, "max_metric_value": 155.269957298, "training_avg": 149.83625, "training_stddev": 1.811235766, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN, the last average value is 148.325. The average for this metric is 149.836.", "is_anomalous": false}, {"value": 148.975, "average": 149.785588235, "min_value": 144.487235325, "max_value": 155.083941146, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "b7ea4763cd3c616b254955131ce3a4f9", "metric_id": "1b2687d7342d68669f2263a0954e0ac6", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "detected_at": "2023-01-02T10:44:21.823000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MIN", "metric_name": "average", "anomaly_score": -0.458966163, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 148.975, "min_metric_value": 144.487235325, "max_metric_value": 155.083941146, "training_avg": 149.785588235, "training_stddev": 1.766117637, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN, the last average value is 148.975. The average for this metric is 149.786.", "is_anomalous": false}, {"value": 153.535, "average": 149.993888889, "min_value": 144.210268763, "max_value": 155.777509015, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "d329fafb060bc42878344c7879ebb11a", "metric_id": "2c7faa49603f2c6dee0fcd035d11aba6", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "detected_at": "2023-01-02T10:44:21.823000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MIN", "metric_name": "average", "anomaly_score": 1.836796522, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 153.535, "min_metric_value": 144.210268763, "max_metric_value": 155.777509015, "training_avg": 149.993888889, "training_stddev": 1.927873375, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN, the last average value is 153.535. The average for this metric is 149.994.", "is_anomalous": false}, {"value": 146.89, "average": 149.830526316, "min_value": 143.817586322, "max_value": 155.84346631, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "8352c485b6fd90bc35d53fae73413e7e", "metric_id": "44e38602e495a4614375f37cc77c2d9d", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "detected_at": "2023-01-02T10:44:21.823000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MIN", "metric_name": "average", "anomaly_score": -1.467099116, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 146.89, "min_metric_value": 143.817586322, "max_metric_value": 155.84346631, "training_avg": 149.830526316, "training_stddev": 2.004313331, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN, the last average value is 146.89. The average for this metric is 149.831.", "is_anomalous": false}, {"value": 152.89, "average": 149.9835, "min_value": 143.781508406, "max_value": 156.185491594, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "e055a906079285fd381461a1cce06e3b", "metric_id": "febbe34115778bafb585c6af806310f6", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "detected_at": "2023-01-02T10:44:21.823000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MIN", "metric_name": "average", "anomaly_score": 1.405919351, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 152.89, "min_metric_value": 143.781508406, "max_metric_value": 156.185491594, "training_avg": 149.9835, "training_stddev": 2.067330531, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN, the last average value is 152.89. The average for this metric is 149.984.", "is_anomalous": false}, {"value": 147.57, "average": 149.868571429, "min_value": 143.620540882, "max_value": 156.116601975, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "3a7569c0db6ba908f29d8eb659b87eb1", "metric_id": "91660bad8ce859a270efa8eb952772a8", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "detected_at": "2023-01-02T10:44:21.823000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MIN", "metric_name": "average", "anomaly_score": -1.103662063, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 147.57, "min_metric_value": 143.620540882, "max_metric_value": 156.116601975, "training_avg": 149.868571429, "training_stddev": 2.082676849, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN, the last average value is 147.57. The average for this metric is 149.869.", "is_anomalous": false}, {"value": 150.12, "average": 149.88, "min_value": 143.780426244, "max_value": 155.979573756, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "9ca26513e17d12462c0a0508a2a5f044", "metric_id": "1bcbf6bd87d221a4785222d8d473d85b", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "detected_at": "2023-01-02T10:44:21.823000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MIN", "metric_name": "average", "anomaly_score": 0.1180410351, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 150.12, "min_metric_value": 143.780426244, "max_metric_value": 155.979573756, "training_avg": 149.88, "training_stddev": 2.033191252, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN, the last average value is 150.12. The average for this metric is 149.88.", "is_anomalous": false}, {"value": 147.825, "average": 149.790652174, "min_value": 143.694246612, "max_value": 155.887057735, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "fec429f8101e61f53bb1ef86c9a5a097", "metric_id": "39c613a8888b84e546b075b42ebef074", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "detected_at": "2023-01-02T10:44:21.823000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MIN", "metric_name": "average", "anomaly_score": -0.9672841582, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 147.825, "min_metric_value": 143.694246612, "max_metric_value": 155.887057735, "training_avg": 149.790652174, "training_stddev": 2.032135187, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN, the last average value is 147.825. The average for this metric is 149.791.", "is_anomalous": false}, {"value": 147.595, "average": 149.699166667, "min_value": 143.587041511, "max_value": 155.811291822, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "38983149226dcd6ac3d782652031eb92", "metric_id": "29a4b075a046fdab97d6fc6e64115afc", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "detected_at": "2023-01-02T10:44:21.823000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MIN", "metric_name": "average", "anomaly_score": -1.032783171, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 147.595, "min_metric_value": 143.587041511, "max_metric_value": 155.811291822, "training_avg": 149.699166667, "training_stddev": 2.037375052, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN, the last average value is 147.595. The average for this metric is 149.699.", "is_anomalous": false}, {"value": 152.085, "average": 149.7946, "min_value": 143.64230939, "max_value": 155.94689061, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "906876afc71a9f5199158bf19a1cd5b7", "metric_id": "f726a9bdd4d8261a1ef40e8488845169", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "detected_at": "2023-01-02T10:44:21.823000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MIN", "metric_name": "average", "anomaly_score": 1.116852313, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 152.085, "min_metric_value": 143.64230939, "max_metric_value": 155.94689061, "training_avg": 149.7946, "training_stddev": 2.050763537, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN, the last average value is 152.085. The average for this metric is 149.795.", "is_anomalous": false}, {"value": 151.455, "average": 149.858461538, "min_value": 143.751827945, "max_value": 155.965095132, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "6bd5512bfafaae765d58ef251128edcf", "metric_id": "ff81894a21e787e40318b0b8938b1cf5", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "detected_at": "2023-01-02T10:44:21.823000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MIN", "metric_name": "average", "anomaly_score": 0.7843299113, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 151.455, "min_metric_value": 143.751827945, "max_metric_value": 155.965095132, "training_avg": 149.858461538, "training_stddev": 2.035544531, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN, the last average value is 151.455. The average for this metric is 149.858.", "is_anomalous": false}, {"value": 149.94, "average": 149.861481481, "min_value": 143.873249539, "max_value": 155.849713424, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "31d6470b5662a47d0aff01f6d929663b", "metric_id": "af75338ef8d9d232d01579f924642ca6", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "detected_at": "2023-01-02T10:44:21.823000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MIN", "metric_name": "average", "anomaly_score": 0.03933641145, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 149.94, "min_metric_value": 143.873249539, "max_metric_value": 155.849713424, "training_avg": 149.861481481, "training_stddev": 1.996077314, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN, the last average value is 149.94. The average for this metric is 149.861.", "is_anomalous": false}, {"value": 148.945, "average": 149.82875, "min_value": 143.929530242, "max_value": 155.727969758, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "a03f4f67ad047c612307224235d51d45", "metric_id": "6aa342d8cfd23a3beb30bc72e2bf5b07", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "detected_at": "2023-01-02T10:44:21.823000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MIN", "metric_name": "average", "anomaly_score": -0.4494238406, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 148.945, "min_metric_value": 143.929530242, "max_metric_value": 155.727969758, "training_avg": 149.82875, "training_stddev": 1.966406586, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN, the last average value is 148.945. The average for this metric is 149.829.", "is_anomalous": false}, {"value": 150.84, "average": 149.86362069, "min_value": 144.043373691, "max_value": 155.683867688, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "5fc1129338c283dee3d305c4f1ec227c", "metric_id": "580f40899b4b2682f0764087f97b45bd", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "detected_at": "2023-01-02T10:44:21.823000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MIN", "metric_name": "average", "anomaly_score": 0.5032669459, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 150.84, "min_metric_value": 144.043373691, "max_metric_value": 155.683867688, "training_avg": 149.86362069, "training_stddev": 1.940082333, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN, the last average value is 150.84. The average for this metric is 149.864.", "is_anomalous": false}, {"value": 102.795, "average": 148.294666667, "min_value": 144.043373691, "max_value": 155.683867688, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "4142c7bc09aad91bafdda978bbb422ac", "metric_id": "27d46b4993ea0fc408d5d51e687e667f", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "detected_at": "2023-01-02T10:44:21.823000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "MIN", "metric_name": "average", "anomaly_score": -5.16899409, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 102.795, "min_metric_value": 121.887401032, "max_metric_value": 174.701932301, "training_avg": 148.294666667, "training_stddev": 8.802421878, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN, the last average value is 102.795. The average for this metric is 148.295.", "is_anomalous": true}], "result_description": "In column MIN, the last average value is 102.795. The average for this metric is 148.295."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": "AVERAGE", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:23+02:00", "latest_run_time_utc": "2023-01-02T10:44:23+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "max", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_max__average__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('max' as varchar)\n and upper(column_name) = upper(cast('AVERAGE' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column AVERAGE, the last max value is 110. The average for this metric is 101.3.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_max__average__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('max' as varchar)\n and upper(column_name) = upper(cast('AVERAGE' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Max", "metrics": [{"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "5f8b4cee3d53c44d39dfbf4a8af93111", "metric_id": "332b83620ff4fd4d1375ee981e4dd652", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "detected_at": "2023-01-02T10:44:22.236000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "ccea1f207723306bba305a22a8685968", "metric_id": "4c681518fcaca8f2c6b645b1e43d6a48", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "detected_at": "2023-01-02T10:44:22.236000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "5d8814f9da926bb527581e03314998d2", "metric_id": "bf51970b4acca5f7852960cde6a909c8", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "detected_at": "2023-01-02T10:44:22.236000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "8fbcff9b5c69299912b54d878c01de71", "metric_id": "363ea8b4058558159f07dc2ac13a4fa1", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "detected_at": "2023-01-02T10:44:22.236000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "ce4344252c1bd5982cbd4a21f022a61c", "metric_id": "97d6b9660575bbd5af34444f17605246", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "detected_at": "2023-01-02T10:44:22.236000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "92913e6fcf7800e0f9ec916cffe395c8", "metric_id": "beea7526708760acf05a02728beb0a14", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "detected_at": "2023-01-02T10:44:22.236000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "2070a22b75702f34ef93d4c7be14de66", "metric_id": "d9537828d6bb28e1c69bf64b2cd70503", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "detected_at": "2023-01-02T10:44:22.236000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "8d34aa3dd18aefc14e0797f654d751ec", "metric_id": "3c0bda8ae82e9e128d48a3f2ded33509", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "detected_at": "2023-01-02T10:44:22.236000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "25731bfcd2ada90c0b29753c4d29bc3a", "metric_id": "87f22a9160a30bc7bd506cdc3bc5c7e7", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "detected_at": "2023-01-02T10:44:22.236000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "5d37b86db62071ba708661cd5831e1eb", "metric_id": "526bb8e718cf1a0d01a7d5f23b1fb86c", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "detected_at": "2023-01-02T10:44:22.236000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "6a69cf150830a736927eacef278a81da", "metric_id": "bb41fc1be9759f093be2a083744816bd", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "detected_at": "2023-01-02T10:44:22.236000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "30b53b20ba1d52c5c0bd3da85931d449", "metric_id": "9591ad2d7f2235760f238c2b235fecf3", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "detected_at": "2023-01-02T10:44:22.236000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "f297f1f417589a4dc3c50dbe41625f84", "metric_id": "6623b118745b0b7150feca2087cd4784", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "detected_at": "2023-01-02T10:44:22.236000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "6653063d9e53dbd2686efe3e50add51a", "metric_id": "a9c9f9c56745f2166db891eeca1d7b59", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "detected_at": "2023-01-02T10:44:22.236000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "a57ef805eaace9eaa38cc17b6df4a2c8", "metric_id": "4bb4a5d7dc0763bba4da4f3cf32e982e", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "detected_at": "2023-01-02T10:44:22.236000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "4a55a3d39481fc63e504cadf4d5f4a72", "metric_id": "fd9d57908798ed180b1fdc5c3a7f4b0f", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "detected_at": "2023-01-02T10:44:22.236000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "c7c2d8da380665717ed52cc5d4fa41bd", "metric_id": "840ebd77662cc807e62666a1a531f96a", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "detected_at": "2023-01-02T10:44:22.236000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "1c0208ffe5bad14d37bcf6c7324ee4ff", "metric_id": "65e3225ae129e18e0ac9a1556fa55c88", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "detected_at": "2023-01-02T10:44:22.236000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "6c771ff60d5f522cf3df935f085d2abc", "metric_id": "fa5331c665f53b94d34d1884dad43830", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "detected_at": "2023-01-02T10:44:22.236000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "0794db24e8843d86c46d19ac3bd80c86", "metric_id": "f50bb8eb6faf5184c8fe254cb105e0e7", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "detected_at": "2023-01-02T10:44:22.236000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "ccaae5a2fba111a697b67d22ba299412", "metric_id": "5f570563021643c79a2e5eb972b76e3e", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "detected_at": "2023-01-02T10:44:22.236000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "d0d75e112e52e0d861ddc395cdd12dc7", "metric_id": "89bc93d09b8f5a544276a3dbcda4ab3e", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "detected_at": "2023-01-02T10:44:22.236000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "c3ad411094e45a7655ebe44cc5c85e59", "metric_id": "c81dc8c7d103aa859e3073c9dcf9ca5a", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "detected_at": "2023-01-02T10:44:22.236000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "f2b79871719267a7e6ebc3a5007e5d9e", "metric_id": "5cb67881b4bf64ea5b0a556b31f85ddd", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "detected_at": "2023-01-02T10:44:22.236000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "37f5046a84b50df737e71c2a139af19d", "metric_id": "7c104a1e48621ac628463b908518f60b", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "detected_at": "2023-01-02T10:44:22.236000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "f36f749327359e53494debe2db3d3f68", "metric_id": "4892157402b6279fcd1b9f64687f840b", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "detected_at": "2023-01-02T10:44:22.236000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "ef09ec42cbfb0c3e21a6222dcd3b8833", "metric_id": "8e158ebd1c7424dd3001bf882e5d361d", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "detected_at": "2023-01-02T10:44:22.236000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 101.0, "average": 101.0, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "1b5bf6ded825dbe6f8d6ac8e199ed982", "metric_id": "9bb6c2c7468a41bc79524d14e8f01062", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "detected_at": "2023-01-02T10:44:22.236000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 101.0, "min_metric_value": 101.0, "max_metric_value": 101.0, "training_avg": 101.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last max value is 101. The average for this metric is 101.", "is_anomalous": false}, {"value": 110.0, "average": 101.3, "min_value": 101.0, "max_value": 101.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "3c1f6892e3a6a91fec48d55af9be8cde", "metric_id": "e513c279dc8b5e306130928b02e02600", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "detected_at": "2023-01-02T10:44:22.236000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "AVERAGE", "metric_name": "max", "anomaly_score": 5.294651389, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 110.0, "min_metric_value": 96.370496982, "max_metric_value": 106.229503018, "training_avg": 101.3, "training_stddev": 1.643167673, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE, the last max value is 110. The average for this metric is 101.3.", "is_anomalous": true}], "result_description": "In column AVERAGE, the last max value is 110. The average for this metric is 101.3."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": "ZERO_PERCENT", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:24+02:00", "latest_run_time_utc": "2023-01-02T10:44:24+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_zero_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('ZERO_PERCENT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column ZERO_PERCENT, the last null_percent value is 0. The average for this metric is 0.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_zero_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('ZERO_PERCENT' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Null Percent", "metrics": [{"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "21ae6e34b82c6845d58ac084ecceb597", "metric_id": "2afcfdac45b531d91101757233129352", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "287650f45e40e26c7ed49ef41083ea17", "metric_id": "b1086753f9d6c0313687c3b15ceaaa82", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "b306b50da893ee9e31cfeed3b7b5beba", "metric_id": "f71f88a775defb7a60f5354c8f947cb3", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "b2628a8a290baf31d1b7938562374deb", "metric_id": "a8fde6108e0960963eb8475172900d93", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "9916087a72965c967ab8c7892b9cefe0", "metric_id": "3d4ec5a177113fcaac045e90dd6ae6e6", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "75cec7692ac6d7bbcc0edfc1b9ab1702", "metric_id": "131751dfca3b451ed8319db79336066d", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "4ca874d4d7f0a696dd6f61f65fd64592", "metric_id": "f42004fd232b9ff4d7b37d4fac605200", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "3c8a9ef9b96049ccec4b9b79df504f0d", "metric_id": "58047fe12277802a430032e577decc34", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "ff7fd542e51a79293c6cb7864bce6d7c", "metric_id": "e0f9da52c06bf1b29c08a2cc8338c147", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "3939a25291a9b13c4ff3066a78a652be", "metric_id": "8f5c9eb3be56f3f3262773c3e925a913", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "31d8e13248706b262dcf04669cc251b9", "metric_id": "6f153735168e36f660b9a91a1e882568", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "c45557c3c1f04a6bafb5fc3d1ce27db5", "metric_id": "349bb44cce44dd5c2342b6c2d639d321", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "7dd7b868a74112a68711bfa676217863", "metric_id": "efb57af7cd3dbcf0dcad262cf2998901", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "e7825d08661edcdadc52194433883b17", "metric_id": "582e37d892264752fd29f6bd5ed1c7a1", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "fb320d1b7eb22f5dce099d3cae288746", "metric_id": "073f61d2fe32fb9f556a0ea82e08fd8e", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "b06dda25cefa5eeb8d3236285d415916", "metric_id": "8dae5d6fcd6d5bbcf4ff3217ae560c8c", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "74d93fb6bc815076c399dd89980c0481", "metric_id": "5c88fe1377402b2e058acc9f607ba374", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "4efc39d54a0de2e730b39e7e8153403e", "metric_id": "70e69cfd4a039565dc2f1613f098e2a9", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "7f89acb01e81f63d885c6a731aa7c9be", "metric_id": "063365105d6ee0b290e566daff4165b7", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "293ca3c376d0e5d6f5d1aa678745d7c2", "metric_id": "45ee47be82a9bf2da02a844034fec024", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "b3f32f81f4111112b1dea18dbaa071db", "metric_id": "0ada80079ec63a839cb63d5ac79d41e3", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "2b5adf0d01b4aa55a048bdfe01fa8033", "metric_id": "3da3a8e4652f1518e5a144df35aec408", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "2a1e60af0948f1b031e14e6a9307f5eb", "metric_id": "28b6777e35642b4e3974551e135ee86c", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "a93687cbd17d01580ff6f7f73958752d", "metric_id": "ce01d0cf259ad1b0815c6e9ecdb2d43a", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "dc62f0594af5a916f626fb076bacf313", "metric_id": "146043093999eff3ae5e62c285a27c3d", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "1f7cd33367b21943b68557a753483827", "metric_id": "dc01a49272848543bdde5452eea2dfeb", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "7e0f9d1a499dcffed7123fdf9956df7b", "metric_id": "dce9f822fb5ffeec7c8e1a0bc8a42172", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "75794b3cc54a6d1ae53f0e171be95776", "metric_id": "51b8708b0dec4e9bba59e2aec4236e2a", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "befa645e7cb743dfbba8e08fc498cdf7", "metric_id": "10e5f9fe8bba3fbed215e508357f8d4e", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}], "result_description": "In column ZERO_PERCENT, the last null_percent value is 0. The average for this metric is 0."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": "ZERO_PERCENT", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:24+02:00", "latest_run_time_utc": "2023-01-02T10:44:24+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "standard_deviation", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_zero_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('standard_deviation' as varchar)\n and upper(column_name) = upper(cast('ZERO_PERCENT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column ZERO_PERCENT, the last standard_deviation value is 76.527. The average for this metric is 65.456.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_zero_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('standard_deviation' as varchar)\n and upper(column_name) = upper(cast('ZERO_PERCENT' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Standard Deviation", "metrics": [{"value": 68.396379279, "average": 68.03394984, "min_value": 66.496291955, "max_value": 69.571607725, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "11a4f69d6abdb3bf23611252fa3ed4c1", "metric_id": "44c5fbabdecb28ef8399f17e99daed17", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "standard_deviation", "anomaly_score": 0.7071067812, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 68.396379279, "min_metric_value": 66.496291955, "max_metric_value": 69.571607725, "training_avg": 68.03394984, "training_stddev": 0.5125526282, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last standard_deviation value is 68.396. The average for this metric is 68.034.", "is_anomalous": false}, {"value": 64.831197706, "average": 66.966365795, "min_value": 61.313485151, "max_value": 72.61924644, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "455ebe987f6014c61b48c79ca64eb185", "metric_id": "02f0e0ab8754e369c1e9789f65242d83", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "standard_deviation", "anomaly_score": -1.13313984, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 64.831197706, "min_metric_value": 61.313485151, "max_metric_value": 72.61924644, "training_avg": 66.966365795, "training_stddev": 1.884293548, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last standard_deviation value is 64.831. The average for this metric is 66.966.", "is_anomalous": false}, {"value": 67.241318769, "average": 67.035104039, "min_value": 62.401156357, "max_value": 71.66905172, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "a68dda04f94466f2f120fe73fea3c916", "metric_id": "60884e4bdb9532acdef10cd9964d4a1b", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "standard_deviation", "anomaly_score": 0.1335026273, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 67.241318769, "min_metric_value": 62.401156357, "max_metric_value": 71.66905172, "training_avg": 67.035104039, "training_stddev": 1.544649227, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last standard_deviation value is 67.241. The average for this metric is 67.035.", "is_anomalous": false}, {"value": 66.668458184, "average": 66.961774868, "min_value": 62.918623143, "max_value": 71.004926592, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "c2d930b0fd152a02a480a0b90c71aec2", "metric_id": "723f333032a5d758911306c71403fb70", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "standard_deviation", "anomaly_score": -0.2176396317, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 66.668458184, "min_metric_value": 62.918623143, "max_metric_value": 71.004926592, "training_avg": 66.961774868, "training_stddev": 1.347717241, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last standard_deviation value is 66.668. The average for this metric is 66.962.", "is_anomalous": false}, {"value": 65.062658435, "average": 66.645255462, "min_value": 62.345531572, "max_value": 70.944979352, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "5511b9c102ff9c37a67b34f5348523bb", "metric_id": "e6349de2887d864bbe7b42db51e5e0f3", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "standard_deviation", "anomaly_score": -1.104208364, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 65.062658435, "min_metric_value": 62.345531572, "max_metric_value": 70.944979352, "training_avg": 66.645255462, "training_stddev": 1.433241297, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last standard_deviation value is 65.063. The average for this metric is 66.645.", "is_anomalous": false}, {"value": 66.614728177, "average": 66.640894421, "min_value": 62.715648851, "max_value": 70.566139991, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "63aefa593128cfffc91650eb18955ca2", "metric_id": "be88d6477f43714cfb06146ebee77b17", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "standard_deviation", "anomaly_score": -0.01999842629, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 66.614728177, "min_metric_value": 62.715648851, "max_metric_value": 70.566139991, "training_avg": 66.640894421, "training_stddev": 1.30841519, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last standard_deviation value is 66.615. The average for this metric is 66.641.", "is_anomalous": false}, {"value": 64.574992437, "average": 66.382656673, "min_value": 62.139082861, "max_value": 70.626230486, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "b9f97c7b0892c6e0c21c155322a8934c", "metric_id": "6dbe1c7dfe8725be27981302ef79844c", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "standard_deviation", "anomaly_score": -1.277930572, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 64.574992437, "min_metric_value": 62.139082861, "max_metric_value": 70.626230486, "training_avg": 66.382656673, "training_stddev": 1.414524604, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last standard_deviation value is 64.575. The average for this metric is 66.383.", "is_anomalous": false}, {"value": 65.87209702, "average": 66.325927823, "min_value": 62.323728425, "max_value": 70.328127221, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "d05bd6a3f54d8775771144049120b442", "metric_id": "eee1842812f9be12d4a3d3b65f99ffb5", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "standard_deviation", "anomaly_score": -0.340186051, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 65.87209702, "min_metric_value": 62.323728425, "max_metric_value": 70.328127221, "training_avg": 66.325927823, "training_stddev": 1.334066466, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last standard_deviation value is 65.872. The average for this metric is 66.326.", "is_anomalous": false}, {"value": 66.642298729, "average": 66.357564914, "min_value": 62.572337287, "max_value": 70.14279254, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "18e165f406c72bafb0921825af23cb86", "metric_id": "64471965b0c1fea67b562f68b9d75549", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "standard_deviation", "anomaly_score": 0.2256671278, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 66.642298729, "min_metric_value": 62.572337287, "max_metric_value": 70.14279254, "training_avg": 66.357564914, "training_stddev": 1.261742542, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last standard_deviation value is 66.642. The average for this metric is 66.358.", "is_anomalous": false}, {"value": 66.464677588, "average": 66.367302429, "min_value": 62.775013401, "max_value": 69.959591458, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "ec4f4068478e01cfd56c9008bbee9ece", "metric_id": "4b9a5475bdcab40baa66c3fc49e58a3b", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "standard_deviation", "anomaly_score": 0.0813201478, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 66.464677588, "min_metric_value": 62.775013401, "max_metric_value": 69.959591458, "training_avg": 66.367302429, "training_stddev": 1.197429676, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last standard_deviation value is 66.465. The average for this metric is 66.367.", "is_anomalous": false}, {"value": 67.392033699, "average": 66.452696702, "min_value": 62.914483021, "max_value": 69.990910383, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "b5bcbb0f352ef62159094987293de7cd", "metric_id": "a3adf0950027103d8ac121a46ff865f1", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "standard_deviation", "anomaly_score": 0.7964501989, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 67.392033699, "min_metric_value": 62.914483021, "max_metric_value": 69.990910383, "training_avg": 66.452696702, "training_stddev": 1.17940456, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last standard_deviation value is 67.392. The average for this metric is 66.453.", "is_anomalous": false}, {"value": 65.770714107, "average": 66.400236502, "min_value": 62.965458168, "max_value": 69.835014836, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "a9a7f78c679bdaa37ce3270673a6fca9", "metric_id": "9ee89f127067c1fc13a40480d0e782cd", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "standard_deviation", "anomaly_score": -0.5498367002, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 65.770714107, "min_metric_value": 62.965458168, "max_metric_value": 69.835014836, "training_avg": 66.400236502, "training_stddev": 1.144926111, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last standard_deviation value is 65.771. The average for this metric is 66.4.", "is_anomalous": false}, {"value": 66.702769035, "average": 66.421845969, "min_value": 63.11291491, "max_value": 69.730777027, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "6a981ae7c814cb4eb7d5f73f65a43b51", "metric_id": "7c0085fcfa87bb59d9bfe9793619beae", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "standard_deviation", "anomaly_score": 0.2546953032, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 66.702769035, "min_metric_value": 63.11291491, "max_metric_value": 69.730777027, "training_avg": 66.421845969, "training_stddev": 1.102977019, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last standard_deviation value is 66.703. The average for this metric is 66.422.", "is_anomalous": false}, {"value": 65.432799353, "average": 66.355909528, "min_value": 63.076598704, "max_value": 69.635220352, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "7019a8fd7473f66358797a9daebca1ce", "metric_id": "1587ef6f22af58ea244b865ab3d3ceab", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "standard_deviation", "anomaly_score": -0.8444855257, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 65.432799353, "min_metric_value": 63.076598704, "max_metric_value": 69.635220352, "training_avg": 66.355909528, "training_stddev": 1.093103608, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last standard_deviation value is 65.433. The average for this metric is 66.356.", "is_anomalous": false}, {"value": 67.134156544, "average": 66.404549966, "min_value": 63.18311509, "max_value": 69.625984843, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "946fed6530f42ae117291d487b93727b", "metric_id": "92726e780bfbf91dd04be96e045a5afe", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "standard_deviation", "anomaly_score": 0.6794549065, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 67.134156544, "min_metric_value": 63.18311509, "max_metric_value": 69.625984843, "training_avg": 66.404549966, "training_stddev": 1.073811626, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last standard_deviation value is 67.134. The average for this metric is 66.405.", "is_anomalous": false}, {"value": 62.284964954, "average": 66.162221436, "min_value": 61.836290689, "max_value": 70.488152183, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "2944a88c4f01e8b845c24aff615b2bed", "metric_id": "15f4f5f5d82acfc102a61de9e5531532", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "standard_deviation", "anomaly_score": -2.688847817, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 62.284964954, "min_metric_value": 61.836290689, "max_metric_value": 70.488152183, "training_avg": 66.162221436, "training_stddev": 1.441976916, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last standard_deviation value is 62.285. The average for this metric is 66.162.", "is_anomalous": false}, {"value": 64.87954649, "average": 66.090961717, "min_value": 61.797303624, "max_value": 70.38461981, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "37a226458e8dcac88195533281dae2e1", "metric_id": "183f63d52899902a1103a30e95916180", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "standard_deviation", "anomaly_score": -0.8464217695, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 64.87954649, "min_metric_value": 61.797303624, "max_metric_value": 70.38461981, "training_avg": 66.090961717, "training_stddev": 1.431219364, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last standard_deviation value is 64.88. The average for this metric is 66.091.", "is_anomalous": false}, {"value": 60.031644712, "average": 65.772050296, "min_value": 59.872662703, "max_value": 71.671437888, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "e8ec9413fc3e4cf6d19aa2ffd922e917", "metric_id": "46b89444c57e7e7e117beed8636226ee", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "standard_deviation", "anomaly_score": -2.919153299, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 60.031644712, "min_metric_value": 59.872662703, "max_metric_value": 71.671437888, "training_avg": 65.772050296, "training_stddev": 1.966462531, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last standard_deviation value is 60.032. The average for this metric is 65.772.", "is_anomalous": false}, {"value": 64.186345391, "average": 65.69276505, "min_value": 59.853025669, "max_value": 71.532504432, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "e0446c4b4fdf3e46c82be1015b8636a4", "metric_id": "291a4e4c16078bf403fbc8b7f2a94ea7", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "standard_deviation", "anomaly_score": -0.773880251, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 64.186345391, "min_metric_value": 59.853025669, "max_metric_value": 71.532504432, "training_avg": 65.69276505, "training_stddev": 1.946579794, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last standard_deviation value is 64.186. The average for this metric is 65.693.", "is_anomalous": false}, {"value": 58.299030737, "average": 65.340682464, "min_value": 57.868984255, "max_value": 72.812380674, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "2eced0398175118682a12e13175980b7", "metric_id": "e13ecc5a47fd5e45bdee12156c565295", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "standard_deviation", "anomaly_score": -2.827329823, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 58.299030737, "min_metric_value": 57.868984255, "max_metric_value": 72.812380674, "training_avg": 65.340682464, "training_stddev": 2.49056607, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last standard_deviation value is 58.299. The average for this metric is 65.341.", "is_anomalous": false}, {"value": 65.636436596, "average": 65.354125834, "min_value": 58.060041689, "max_value": 72.648209979, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "059cda13c31ffb9126e437ce9b1248ea", "metric_id": "0ac9e47eb516a9aa2be0bb35516d26b5", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "standard_deviation", "anomaly_score": 0.1161122179, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 65.636436596, "min_metric_value": 58.060041689, "max_metric_value": 72.648209979, "training_avg": 65.354125834, "training_stddev": 2.431361382, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last standard_deviation value is 65.636. The average for this metric is 65.354.", "is_anomalous": false}, {"value": 60.976501776, "average": 65.163794353, "min_value": 57.529391154, "max_value": 72.798197552, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "9b30686c9631ae6c27d9ebe9260cf3f0", "metric_id": "119f5d949c126d82a667c56bf1b2e7ab", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "standard_deviation", "anomaly_score": -1.64543022, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 60.976501776, "min_metric_value": 57.529391154, "max_metric_value": 72.798197552, "training_avg": 65.163794353, "training_stddev": 2.544801066, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last standard_deviation value is 60.977. The average for this metric is 65.164.", "is_anomalous": false}, {"value": 63.245185925, "average": 65.083852335, "min_value": 57.525385743, "max_value": 72.642318927, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "9c57e82854e5ea6cb0e6da365fc35280", "metric_id": "b057b0e11dd0f8419ef5a886b56eb473", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "standard_deviation", "anomaly_score": -0.7297775498, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 63.245185925, "min_metric_value": 57.525385743, "max_metric_value": 72.642318927, "training_avg": 65.083852335, "training_stddev": 2.519488864, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last standard_deviation value is 63.245. The average for this metric is 65.084.", "is_anomalous": false}, {"value": 64.423126251, "average": 65.057423292, "min_value": 57.647487773, "max_value": 72.467358811, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "5b1bf85c17debb85ef0009606043eb87", "metric_id": "1e5d0c23c3e13fb31ac814b7c002d878", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "standard_deviation", "anomaly_score": -0.2568026559, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 64.423126251, "min_metric_value": 57.647487773, "max_metric_value": 72.467358811, "training_avg": 65.057423292, "training_stddev": 2.469978506, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last standard_deviation value is 64.423. The average for this metric is 65.057.", "is_anomalous": false}, {"value": 64.658279171, "average": 65.042071595, "min_value": 57.778050224, "max_value": 72.306092966, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "1deca82a3d07642b6b49bde4e814a258", "metric_id": "742e5c5f168453f809849bee611c3b52", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "standard_deviation", "anomaly_score": -0.1585041141, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 64.658279171, "min_metric_value": 57.778050224, "max_metric_value": 72.306092966, "training_avg": 65.042071595, "training_stddev": 2.421340457, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last standard_deviation value is 64.658. The average for this metric is 65.042.", "is_anomalous": false}, {"value": 69.671227921, "average": 65.213521829, "min_value": 57.605659178, "max_value": 72.821384481, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "fe336a5c387f0ce2cd480fee3c315e27", "metric_id": "3493d569d377d7a484a74077b6a761fb", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "standard_deviation", "anomaly_score": 1.757802275, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 69.671227921, "min_metric_value": 57.605659178, "max_metric_value": 72.821384481, "training_avg": 65.213521829, "training_stddev": 2.535954217, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last standard_deviation value is 69.671. The average for this metric is 65.214.", "is_anomalous": false}, {"value": 60.073462356, "average": 65.029948277, "min_value": 57.015704894, "max_value": 73.044191659, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "e2faf7496d1b596d9ed3dfa893c4e238", "metric_id": "934e07f38fc694a4f2679930e2d0f35e", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "standard_deviation", "anomaly_score": -1.855378861, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 60.073462356, "min_metric_value": 57.015704894, "max_metric_value": 73.044191659, "training_avg": 65.029948277, "training_stddev": 2.671414461, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last standard_deviation value is 60.073. The average for this metric is 65.03.", "is_anomalous": false}, {"value": 66.323225579, "average": 65.074544046, "min_value": 57.171803467, "max_value": 72.977284624, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "cb609105cbea1547418a725f952f6c7a", "metric_id": "9250b144bb09cbd5e30336bd1ff47194", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "standard_deviation", "anomaly_score": 0.4740184198, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 66.323225579, "min_metric_value": 57.171803467, "max_metric_value": 72.977284624, "training_avg": 65.074544046, "training_stddev": 2.63424686, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last standard_deviation value is 66.323. The average for this metric is 65.075.", "is_anomalous": false}, {"value": 76.527045667, "average": 65.4562941, "min_value": 57.171803467, "max_value": 72.977284624, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "19c1cadfb6ab139026a6764437088210", "metric_id": "ceea6f1077a512f87065af437eac706a", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "standard_deviation", "anomaly_score": 3.327091575, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 76.527045667, "min_metric_value": 55.473925428, "max_metric_value": 75.438662772, "training_avg": 65.4562941, "training_stddev": 3.327456224, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last standard_deviation value is 76.527. The average for this metric is 65.456.", "is_anomalous": true}], "result_description": "In column ZERO_PERCENT, the last standard_deviation value is 76.527. The average for this metric is 65.456."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": "ZERO_PERCENT", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:24+02:00", "latest_run_time_utc": "2023-01-02T10:44:24+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "max", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_zero_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('max' as varchar)\n and upper(column_name) = upper(cast('ZERO_PERCENT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column ZERO_PERCENT, the last max value is 199. The average for this metric is 199.633.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_zero_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('max' as varchar)\n and upper(column_name) = upper(cast('ZERO_PERCENT' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Max", "metrics": [{"value": 199.0, "average": 199.5, "min_value": 197.378679656, "max_value": 201.621320344, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "c001692e1eecc6e426677be807403777", "metric_id": "3cdf03585cd080b6b68cc7683afd199d", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "max", "anomaly_score": -0.7071067812, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 199.0, "min_metric_value": 197.378679656, "max_metric_value": 201.621320344, "training_avg": 199.5, "training_stddev": 0.7071067812, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last max value is 199. The average for this metric is 199.5.", "is_anomalous": false}, {"value": 200.0, "average": 199.666666667, "min_value": 197.934615859, "max_value": 201.398717474, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "074beb6dd5c8dc0141d0e524b35dbdfa", "metric_id": "3e7bc9e00133909cfd8fdc00aae36395", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "max", "anomaly_score": 0.5773502692, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 200.0, "min_metric_value": 197.934615859, "max_metric_value": 201.398717474, "training_avg": 199.666666667, "training_stddev": 0.5773502692, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last max value is 200. The average for this metric is 199.667.", "is_anomalous": false}, {"value": 200.0, "average": 199.75, "min_value": 198.25, "max_value": 201.25, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "4f0c7349eb4117e0e13f23d6e6d9f264", "metric_id": "649ad57f06319f3f4816d9e5cb9b0412", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "max", "anomaly_score": 0.5, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 200.0, "min_metric_value": 198.25, "max_metric_value": 201.25, "training_avg": 199.75, "training_stddev": 0.5, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last max value is 200. The average for this metric is 199.75.", "is_anomalous": false}, {"value": 200.0, "average": 199.8, "min_value": 198.458359214, "max_value": 201.141640786, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "23ce839f9b99b0af4242fb087aab71fd", "metric_id": "79ab14296ee9aaa32ab5e53a516a3325", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "max", "anomaly_score": 0.4472135955, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 200.0, "min_metric_value": 198.458359214, "max_metric_value": 201.141640786, "training_avg": 199.8, "training_stddev": 0.4472135955, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last max value is 200. The average for this metric is 199.8.", "is_anomalous": false}, {"value": 200.0, "average": 199.833333333, "min_value": 198.608588462, "max_value": 201.058078205, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "73ff2dabf8589b255731dc3bc2d65eb6", "metric_id": "ff28fcca76ea42573ec147b449279ad0", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "max", "anomaly_score": 0.4082482905, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 200.0, "min_metric_value": 198.608588462, "max_metric_value": 201.058078205, "training_avg": 199.833333333, "training_stddev": 0.4082482905, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last max value is 200. The average for this metric is 199.833.", "is_anomalous": false}, {"value": 199.0, "average": 199.714285714, "min_value": 198.250435605, "max_value": 201.178135824, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "7de729c814d8f7ef69ad512e414f621f", "metric_id": "d6c22d8a51ecc2ca79d1b5be741343c9", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "max", "anomaly_score": -1.463850109, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 199.0, "min_metric_value": 198.250435605, "max_metric_value": 201.178135824, "training_avg": 199.714285714, "training_stddev": 0.4879500365, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last max value is 199. The average for this metric is 199.714.", "is_anomalous": false}, {"value": 200.0, "average": 199.75, "min_value": 198.36126985, "max_value": 201.13873015, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "9634b4baa6f7af093d3c8b59bedfba9c", "metric_id": "7ae3b483e6e8185d87b43463fdf5f931", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "max", "anomaly_score": 0.5400617249, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 200.0, "min_metric_value": 198.36126985, "max_metric_value": 201.13873015, "training_avg": 199.75, "training_stddev": 0.4629100499, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last max value is 200. The average for this metric is 199.75.", "is_anomalous": false}, {"value": 200.0, "average": 199.777777778, "min_value": 198.454902122, "max_value": 201.100653433, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "57dddf24b60894764ed16935d7f29e9c", "metric_id": "b5af6e773f6684344f494738955bf493", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "max", "anomaly_score": 0.5039526307, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 200.0, "min_metric_value": 198.454902122, "max_metric_value": 201.100653433, "training_avg": 199.777777778, "training_stddev": 0.4409585518, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last max value is 200. The average for this metric is 199.778.", "is_anomalous": false}, {"value": 200.0, "average": 199.8, "min_value": 198.535088936, "max_value": 201.064911064, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "2dfe73f7e4f8c13b00df47a270feec3e", "metric_id": "af3068d8f191e449c766ea5ddd1c0125", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "max", "anomaly_score": 0.474341649, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 200.0, "min_metric_value": 198.535088936, "max_metric_value": 201.064911064, "training_avg": 199.8, "training_stddev": 0.4216370214, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last max value is 200. The average for this metric is 199.8.", "is_anomalous": false}, {"value": 200.0, "average": 199.818181818, "min_value": 198.604622066, "max_value": 201.031741571, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "07171b50a3bba73b829486e70761320e", "metric_id": "e52b569ad7ec89b6d1131afd11e6517c", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "max", "anomaly_score": 0.449466575, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 200.0, "min_metric_value": 198.604622066, "max_metric_value": 201.031741571, "training_avg": 199.818181818, "training_stddev": 0.4045199175, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last max value is 200. The average for this metric is 199.818.", "is_anomalous": false}, {"value": 200.0, "average": 199.833333333, "min_value": 198.665584917, "max_value": 201.00108175, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "476110cc7b74bc3b21031e98e54f102b", "metric_id": "2ad9eec3246bb874d4bf42a0a12ad04b", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "max", "anomaly_score": 0.4281744193, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 200.0, "min_metric_value": 198.665584917, "max_metric_value": 201.00108175, "training_avg": 199.833333333, "training_stddev": 0.3892494721, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last max value is 200. The average for this metric is 199.833.", "is_anomalous": false}, {"value": 200.0, "average": 199.846153846, "min_value": 198.719552422, "max_value": 200.97275527, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "43a2d1d768803e5ab20e1c35af4bff14", "metric_id": "21a47660ade2ccbaf0719573abc92673", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "max", "anomaly_score": 0.4096732452, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 200.0, "min_metric_value": 198.719552422, "max_metric_value": 200.97275527, "training_avg": 199.846153846, "training_stddev": 0.3755338081, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last max value is 200. The average for this metric is 199.846.", "is_anomalous": false}, {"value": 200.0, "average": 199.857142857, "min_value": 198.767733298, "max_value": 200.946552416, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "6443fc6fbf806a03c8eccdff4a7a8471", "metric_id": "11a2d37b5bb3723f9bfc9c8264d3d00f", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "max", "anomaly_score": 0.3933978962, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 200.0, "min_metric_value": 198.767733298, "max_metric_value": 200.946552416, "training_avg": 199.857142857, "training_stddev": 0.3631365196, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last max value is 200. The average for this metric is 199.857.", "is_anomalous": false}, {"value": 200.0, "average": 199.866666667, "min_value": 198.811069341, "max_value": 200.922263992, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "9229b9576f8d5cfe86a9a7db190fc256", "metric_id": "683449ee9db96306141f1d0e088874f5", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "max", "anomaly_score": 0.3789323734, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 200.0, "min_metric_value": 198.811069341, "max_metric_value": 200.922263992, "training_avg": 199.866666667, "training_stddev": 0.3518657753, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last max value is 200. The average for this metric is 199.867.", "is_anomalous": false}, {"value": 200.0, "average": 199.875, "min_value": 198.850304923, "max_value": 200.899695077, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "19979f8e2887c1c55766d091ff003f32", "metric_id": "28187709e26adeed9c5a29aa99e10c9e", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "max", "anomaly_score": 0.3659625274, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 200.0, "min_metric_value": 198.850304923, "max_metric_value": 200.899695077, "training_avg": 199.875, "training_stddev": 0.3415650255, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last max value is 200. The average for this metric is 199.875.", "is_anomalous": false}, {"value": 200.0, "average": 199.882352941, "min_value": 198.886036195, "max_value": 200.878669687, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "8393d336376359190ed7b2fbc8dc3e1a", "metric_id": "dee72b331502beb6c82efef5410a53cf", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "max", "anomaly_score": 0.3542459542, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 200.0, "min_metric_value": 198.886036195, "max_metric_value": 200.878669687, "training_avg": 199.882352941, "training_stddev": 0.3321055821, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last max value is 200. The average for this metric is 199.882.", "is_anomalous": false}, {"value": 200.0, "average": 199.888888889, "min_value": 198.918746389, "max_value": 200.859031389, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "8f9bbaa5b75daace3d01bb315ab1135c", "metric_id": "af019c5fc18b95b049a78433df66c2a3", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "max", "anomaly_score": 0.3435921355, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 200.0, "min_metric_value": 198.918746389, "max_metric_value": 200.859031389, "training_avg": 199.888888889, "training_stddev": 0.3233808334, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last max value is 200. The average for this metric is 199.889.", "is_anomalous": false}, {"value": 200.0, "average": 199.894736842, "min_value": 198.948831539, "max_value": 200.840642145, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "517cc879b45ba1b09ab1a7b081c86aea", "metric_id": "2bd3cd8738860852e1616158b3f074ad", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "max", "anomaly_score": 0.3338489304, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 200.0, "min_metric_value": 198.948831539, "max_metric_value": 200.840642145, "training_avg": 199.894736842, "training_stddev": 0.3153017676, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last max value is 200. The average for this metric is 199.895.", "is_anomalous": false}, {"value": 199.0, "average": 199.85, "min_value": 198.750957354, "max_value": 200.949042646, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "02fe218ebfdb696f65f4892749a6d1e2", "metric_id": "aa35a5b3b939190f85bc81492889d069", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "max", "anomaly_score": -2.320201141, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 199.0, "min_metric_value": 198.750957354, "max_metric_value": 200.949042646, "training_avg": 199.85, "training_stddev": 0.3663475485, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last max value is 199. The average for this metric is 199.85.", "is_anomalous": false}, {"value": 198.0, "average": 199.761904762, "min_value": 198.145029469, "max_value": 201.378780055, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "f73cf52238152a08fa7a7418765dbf47", "metric_id": "2cb0d1f4459375d0bdcf86e5a6333bff", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "max", "anomaly_score": -3.269092123, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 198.0, "min_metric_value": 198.145029469, "max_metric_value": 201.378780055, "training_avg": 199.761904762, "training_stddev": 0.5389584311, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last max value is 198. The average for this metric is 199.762.", "is_anomalous": false}, {"value": 199.0, "average": 199.727272727, "min_value": 198.07582708, "max_value": 201.378718375, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "fb440c0bee96f521225e10e5c64427bf", "metric_id": "5ae04d113bdfe5525f8bc2f832b3e423", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "max", "anomaly_score": -1.321156518, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 199.0, "min_metric_value": 198.07582708, "max_metric_value": 201.378718375, "training_avg": 199.727272727, "training_stddev": 0.5504818826, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last max value is 199. The average for this metric is 199.727.", "is_anomalous": false}, {"value": 200.0, "average": 199.739130435, "min_value": 198.116659767, "max_value": 201.361601102, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "3e35ffda1495627beb924ce47f967c6a", "metric_id": "c8dced8cebe783912d8616ea8b847ce4", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "max", "anomaly_score": 0.4823561444, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 200.0, "min_metric_value": 198.116659767, "max_metric_value": 201.361601102, "training_avg": 199.739130435, "training_stddev": 0.5408235559, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last max value is 200. The average for this metric is 199.739.", "is_anomalous": false}, {"value": 199.0, "average": 199.708333333, "min_value": 198.058234522, "max_value": 201.358432145, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "689faab6a0d49849234c139a27c9d6b3", "metric_id": "d59d882ecc9758f331a2e32545275385", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "max", "anomaly_score": -1.287801667, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 199.0, "min_metric_value": 198.058234522, "max_metric_value": 201.358432145, "training_avg": 199.708333333, "training_stddev": 0.5500329371, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last max value is 199. The average for this metric is 199.708.", "is_anomalous": false}, {"value": 200.0, "average": 199.72, "min_value": 198.095192319, "max_value": 201.344807681, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "d6634f37552a16ee55b97a830fe3d5cc", "metric_id": "9dbec53205ee37cc30741d56f23e5273", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "max", "anomaly_score": 0.5169842621, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 200.0, "min_metric_value": 198.095192319, "max_metric_value": 201.344807681, "training_avg": 199.72, "training_stddev": 0.5416025603, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last max value is 200. The average for this metric is 199.72.", "is_anomalous": false}, {"value": 199.0, "average": 199.692307692, "min_value": 198.044932157, "max_value": 201.339683227, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "c5f04a9d96823f6fde537fa705f12946", "metric_id": "5b58469affe23c6943a5e132334723cf", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "max", "anomaly_score": -1.260746583, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 199.0, "min_metric_value": 198.044932157, "max_metric_value": 201.339683227, "training_avg": 199.692307692, "training_stddev": 0.5491251784, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last max value is 199. The average for this metric is 199.692.", "is_anomalous": false}, {"value": 198.0, "average": 199.62962963, "min_value": 197.741746539, "max_value": 201.51751272, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "85cfced49cc2ba30604022d52f83cdf9", "metric_id": "c94f68a12fe7d32625b48993355fa379", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "max", "anomaly_score": -2.589614216, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 198.0, "min_metric_value": 197.741746539, "max_metric_value": 201.51751272, "training_avg": 199.62962963, "training_stddev": 0.6292943636, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last max value is 198. The average for this metric is 199.63.", "is_anomalous": false}, {"value": 200.0, "average": 199.642857143, "min_value": 197.778402671, "max_value": 201.507311614, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "3d987f59620cad8ed11515c4ac770018", "metric_id": "ccdfcf112d26acc0f8b487993ab9481c", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "max", "anomaly_score": 0.5746606248, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 200.0, "min_metric_value": 197.778402671, "max_metric_value": 201.507311614, "training_avg": 199.642857143, "training_stddev": 0.6214848238, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last max value is 200. The average for this metric is 199.643.", "is_anomalous": false}, {"value": 200.0, "average": 199.655172414, "min_value": 197.813535746, "max_value": 201.496809081, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "1824b8b47bbdcefbcc4311223665f707", "metric_id": "a88467bc4c4c7207413b0348b6189059", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "max", "anomaly_score": 0.561719245, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 200.0, "min_metric_value": 197.813535746, "max_metric_value": 201.496809081, "training_avg": 199.655172414, "training_stddev": 0.6138788892, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last max value is 200. The average for this metric is 199.655.", "is_anomalous": false}, {"value": 199.0, "average": 199.633333333, "min_value": 197.788489634, "max_value": 201.478177033, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "9624a23f3c9f8f065ddbcdfecbc620d3", "metric_id": "ee1896173e3065f07ca3a282122568c6", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "max", "anomaly_score": -1.029897547, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 199.0, "min_metric_value": 197.788489634, "max_metric_value": 201.478177033, "training_avg": 199.633333333, "training_stddev": 0.6149478999, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last max value is 199. The average for this metric is 199.633.", "is_anomalous": false}], "result_description": "In column ZERO_PERCENT, the last max value is 199. The average for this metric is 199.633."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_schema_changes_numeric_column_anomalies_.1d9f82cc93", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": null, "test_name": "schema_changes", "test_display_name": "Schema Changes", "latest_run_time": "2023-01-02T12:46:17+02:00", "latest_run_time_utc": "2023-01-02T10:46:17+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "schema_change", "test_sub_type": "generic", "test_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.elementary_test_results\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n \n\n \n \n \n\n \n \n \n\n \n \n \n\n \n\n \n \n \n \n select * from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_SCHEMA_CHANGES_NUMERIC_COLUMN_ANOMALIES___SCHEMA_CHANGES_ALERTS\"", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.elementary_test_results\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n \n\n \n \n \n\n \n \n \n\n \n \n \n\n \n\n \n \n \n \n select * from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_SCHEMA_CHANGES_NUMERIC_COLUMN_ANOMALIES___SCHEMA_CHANGES_ALERTS\""}, "configuration": {"test_name": "schema_changes", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": "STANDARD_DEVIATION", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:25+02:00", "latest_run_time_utc": "2023-01-02T10:44:25+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "zero_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_standard_deviation__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('zero_count' as varchar)\n and upper(column_name) = upper(cast('STANDARD_DEVIATION' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column STANDARD_DEVIATION, the last zero_count value is 0. The average for this metric is 0.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_standard_deviation__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('zero_count' as varchar)\n and upper(column_name) = upper(cast('STANDARD_DEVIATION' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Zero Count", "metrics": [{"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "8022b0913c8f522e53ea32e70794f78e", "metric_id": "cf6ecdd6abba8d9ee914f363e8864109", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "cca03c20fc708004ef681c6cba6287ec", "metric_id": "e3069aa3abaa3be7d1661ccbec105875", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "8ff25c68bfcb7f6da570edf85d81b33e", "metric_id": "0c011cd494001c6f995697190428b03b", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "b5d99fba84ce4e29415babdf9f3bb6ab", "metric_id": "0d4549333398afab2e5a69f979729345", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "be315097df853527cbc687a6cf1e8520", "metric_id": "48d84f51a2c51ce67a5ff323b266d803", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "d2f9570e59b6c2d6655b766cc7bd3246", "metric_id": "92c4d3343b33f3400c905fee58355bfa", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "1285103658cd68a2199fe01209ebcd06", "metric_id": "70cac6ccbd3a6f05ec6d6e7065418ac9", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "2373822328b1a8c4898d598f011c3ee7", "metric_id": "ccb91d80b15313fda413c208fcbc6b09", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "24ef017d0fa1962b9f9b2dfcb33a2102", "metric_id": "c2fe578698bac493bd0e28b9317407a6", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "4ad4434f6a619c5766a5c8db770eeb5f", "metric_id": "cda827beeb71f1b361d19f14bd045d6e", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "20241a2e7b399ad1e46446f7e7e83605", "metric_id": "faad136f87b416fdb326e621c3d08b36", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "ec71c5a20af81acd8207cadcb6d818e8", "metric_id": "36dd58a92bbc31fe22eb014a59ab1f25", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "1bece4a68d1892c600be13e6c059a56e", "metric_id": "31a76a6df7546c7e56970786ab496dd6", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "e5811dd387cbd9531ebd141376e8051f", "metric_id": "2747aad5f05ea077b759d05a68cf18a9", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "bef398ae74bdb31e8848fbaacd9ca67c", "metric_id": "6e8505c3ef0097c3b55327f7b14fa030", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "efc9fd7fca55b116f0a3be2523c1f524", "metric_id": "f8f77901b10271bedea0364bb877698e", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "9404937eebd8305c7a66da187b204056", "metric_id": "e1d1be9235f736007be34ca6d40aacbf", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "661cd77cea0e0c1ff666a89292847b0e", "metric_id": "98a7ec42709cb557fc07365303b8bf03", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "2359e33c8ebb9654a8a4c693a5ab3358", "metric_id": "d104dd752540159ea0fcded91536230d", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "91f45eb9f34e22f285b63608248d7abe", "metric_id": "042571c00ab61638900d2cc3e3e5f803", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "d3e4b08333926be779c7a46d8a45059e", "metric_id": "6aeeab37627be126883953106818dac2", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "fcd4f55ea3ecd5d49c012d2afbb61f2b", "metric_id": "aab3e4ba85a23cf78e972b55c2565dab", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "5d0eafcda68389cacf61ff5102c1eae8", "metric_id": "b3c1aeb82d336155592aab85ecf384c7", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "acb15f36b2d74c616e1a7421452b170e", "metric_id": "e205a8d84a2041e2081190a0b43fad7f", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "ac503767ec09dac526091928f430273b", "metric_id": "6e4a098ad16aaff2d286df4ab501ed7e", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "a1b23cd3964d24698f012c76682902f7", "metric_id": "42a93bdaff50d94140a22227f2d065c4", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "5e4e42f5eded899e837125596022d805", "metric_id": "8f01d622573698819fc0d11475e9ba71", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "cffff6d508eb6e971fddd10861455dd0", "metric_id": "e5a72868b4352b44a05bfc3f3963a68f", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "1ea935caef4f90c6d432fd845dfb3385", "metric_id": "62994d8dd3fba2272d016e020b221424", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "detected_at": "2023-01-02T10:44:24.418000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "STANDARD_DEVIATION", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column STANDARD_DEVIATION, the last zero_count value is 0. The average for this metric is 0.", "is_anomalous": false}], "result_description": "In column STANDARD_DEVIATION, the last zero_count value is 0. The average for this metric is 0."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": "ZERO_PERCENT", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:24+02:00", "latest_run_time_utc": "2023-01-02T10:44:24+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "average", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_zero_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('ZERO_PERCENT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column ZERO_PERCENT, the last average value is 59.135. The average for this metric is 117.842.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_zero_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('ZERO_PERCENT' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Average", "metrics": [{"value": 115.975, "average": 116.1625, "min_value": 115.367004871, "max_value": 116.957995129, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "05e982f10fd4b11e2e4a76e90e19f5f3", "metric_id": "99a3e415978e055049d3d7990f630b00", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "average", "anomaly_score": -0.7071067812, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 115.975, "min_metric_value": 115.367004871, "max_metric_value": 116.957995129, "training_avg": 116.1625, "training_stddev": 0.265165043, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last average value is 115.975. The average for this metric is 116.163.", "is_anomalous": false}, {"value": 116.535, "average": 116.286666667, "min_value": 115.430702298, "max_value": 117.142631035, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "ed4401f4f80b23189fe53b80f5a78c9a", "metric_id": "716b735f53eb6c19a858c10486d67a2d", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "average", "anomaly_score": 0.8703633323, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 116.535, "min_metric_value": 115.430702298, "max_metric_value": 117.142631035, "training_avg": 116.286666667, "training_stddev": 0.2853214561, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last average value is 116.535. The average for this metric is 116.287.", "is_anomalous": false}, {"value": 117.455, "average": 116.57875, "min_value": 114.692031619, "max_value": 118.465468381, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "93a1c82ec32848a9713b73c8a98bb5a4", "metric_id": "096528f50a6318a587f4cc4cb7cd8fab", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "average", "anomaly_score": 1.393292198, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 117.455, "min_metric_value": 114.692031619, "max_metric_value": 118.465468381, "training_avg": 116.57875, "training_stddev": 0.6289061271, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last average value is 117.455. The average for this metric is 116.579.", "is_anomalous": false}, {"value": 122.01, "average": 117.665, "min_value": 110.19726691, "max_value": 125.13273309, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "11ec73c58a846487dd3b3b3ab7f5c98f", "metric_id": "7a9ff021a27b7dbffc00d7c8ff0b6f32", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "average", "anomaly_score": 1.745509627, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 122.01, "min_metric_value": 110.19726691, "max_metric_value": 125.13273309, "training_avg": 117.665, "training_stddev": 2.489244363, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last average value is 122.01. The average for this metric is 117.665.", "is_anomalous": false}, {"value": 120.715, "average": 118.173333333, "min_value": 110.520400562, "max_value": 125.826266104, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "58431bba3d06770e0521565a29dd84be", "metric_id": "49cb67cd497649349c97ceded274df23", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "average", "anomaly_score": 0.9963500566, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 120.715, "min_metric_value": 110.520400562, "max_metric_value": 125.826266104, "training_avg": 118.173333333, "training_stddev": 2.55097759, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last average value is 120.715. The average for this metric is 118.173.", "is_anomalous": false}, {"value": 116.84, "average": 117.982857143, "min_value": 110.834999822, "max_value": 125.130714464, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "b5d806a8a8a3c9c897e07cfa4b38da89", "metric_id": "abc95d41239a4dbe1884e4281bdcb600", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "average", "anomaly_score": -0.4796642231, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 116.84, "min_metric_value": 110.834999822, "max_metric_value": 125.130714464, "training_avg": 117.982857143, "training_stddev": 2.382619107, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last average value is 116.84. The average for this metric is 117.983.", "is_anomalous": false}, {"value": 128.5, "average": 119.2975, "min_value": 106.32716434, "max_value": 132.26783566, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "392bae5372db0bd1b441ace2bbf3d690", "metric_id": "c6c4f6f068321e5169605d9e44c60b35", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "average", "anomaly_score": 2.128510836, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 128.5, "min_metric_value": 106.32716434, "max_metric_value": 132.26783566, "training_avg": 119.2975, "training_stddev": 4.32344522, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last average value is 128.5. The average for this metric is 119.298.", "is_anomalous": false}, {"value": 112.65, "average": 118.558888889, "min_value": 104.724505545, "max_value": 132.393272233, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "ca8e6501fc587407b39df433a7cd7f73", "metric_id": "809d307749eecb13adc3e2727864f72e", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "average", "anomaly_score": -1.281348523, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 112.65, "min_metric_value": 104.724505545, "max_metric_value": 132.393272233, "training_avg": 118.558888889, "training_stddev": 4.611461115, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last average value is 112.65. The average for this metric is 118.559.", "is_anomalous": false}, {"value": 115.2, "average": 118.223, "min_value": 104.796216692, "max_value": 131.649783308, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "f848b69eb97bf1d3ae0b1735721dfa1b", "metric_id": "825335fcacd81335ae8a88f64d7c513c", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "average", "anomaly_score": -0.6754410041, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 115.2, "min_metric_value": 104.796216692, "max_metric_value": 131.649783308, "training_avg": 118.223, "training_stddev": 4.475594436, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last average value is 115.2. The average for this metric is 118.223.", "is_anomalous": false}, {"value": 124.88, "average": 118.828181818, "min_value": 104.738862556, "max_value": 132.91750108, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "19192b0d789d0f87cb1e80c6d8a5c6fc", "metric_id": "c5ea7abc55583c552ccbf7f0ccbac889", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "average", "anomaly_score": 1.288597001, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 124.88, "min_metric_value": 104.738862556, "max_metric_value": 132.91750108, "training_avg": 118.828181818, "training_stddev": 4.696439754, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last average value is 124.88. The average for this metric is 118.828.", "is_anomalous": false}, {"value": 115.835, "average": 118.57875, "min_value": 104.897302521, "max_value": 132.260197479, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "983077a27ff1432f54f09084720c0097", "metric_id": "ee56454936e329e48d7ed56c3d0d8007", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "average", "anomaly_score": -0.6016359024, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 115.835, "min_metric_value": 104.897302521, "max_metric_value": 132.260197479, "training_avg": 118.57875, "training_stddev": 4.560482493, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last average value is 115.835. The average for this metric is 118.579.", "is_anomalous": false}, {"value": 113.61, "average": 118.196538462, "min_value": 104.460617418, "max_value": 131.932459505, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "04392470838bc706a0e29c457f035a73", "metric_id": "4021504626dd8fdc4365631471a8bda8", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "average", "anomaly_score": -1.001724991, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 113.61, "min_metric_value": 104.460617418, "max_metric_value": 131.932459505, "training_avg": 118.196538462, "training_stddev": 4.578640348, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last average value is 113.61. The average for this metric is 118.197.", "is_anomalous": false}, {"value": 120.13, "average": 118.334642857, "min_value": 105.046858968, "max_value": 131.622426746, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "854164af7cd6a1e623e0affabb9da759", "metric_id": "977041287446c177bd782b45c628b51c", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "average", "anomaly_score": 0.4053400833, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 120.13, "min_metric_value": 105.046858968, "max_metric_value": 131.622426746, "training_avg": 118.334642857, "training_stddev": 4.429261296, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last average value is 120.13. The average for this metric is 118.335.", "is_anomalous": false}, {"value": 113.895, "average": 118.038666667, "min_value": 104.780474563, "max_value": 131.29685877, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "1610961a98e97f5500c17c40ca8eef72", "metric_id": "29c44805e97d0ad71e4ff512201b465b", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "average", "anomaly_score": -0.9376089819, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 113.895, "min_metric_value": 104.780474563, "max_metric_value": 131.29685877, "training_avg": 118.038666667, "training_stddev": 4.419397368, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last average value is 113.895. The average for this metric is 118.039.", "is_anomalous": false}, {"value": 118.1, "average": 118.0425, "min_value": 105.23378695, "max_value": 130.85121305, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "bd01925863a0aea33ffbb06333f5e431", "metric_id": "7bc999286e51b2402afb4c163c4d42c9", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "average", "anomaly_score": 0.01346739515, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 118.1, "min_metric_value": 105.23378695, "max_metric_value": 130.85121305, "training_avg": 118.0425, "training_stddev": 4.269571017, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last average value is 118.1. The average for this metric is 118.042.", "is_anomalous": false}, {"value": 121.515, "average": 118.246764706, "min_value": 105.590028669, "max_value": 130.903500742, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "0dd29e860ab61a51756407168259f6d5", "metric_id": "9ed124fadf12d27e9bc47b257a803e32", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "average", "anomaly_score": 0.7746630612, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 121.515, "min_metric_value": 105.590028669, "max_metric_value": 130.903500742, "training_avg": 118.246764706, "training_stddev": 4.218912012, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last average value is 121.515. The average for this metric is 118.247.", "is_anomalous": false}, {"value": 123.785, "average": 118.554444444, "min_value": 105.666237569, "max_value": 131.44265132, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "996fd11e3d2b1621a6b85cb7c1e64578", "metric_id": "b8896200ef5da41331d414344b84dda7", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "average", "anomaly_score": 1.217521321, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 123.785, "min_metric_value": 105.666237569, "max_metric_value": 131.44265132, "training_avg": 118.554444444, "training_stddev": 4.296068959, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last average value is 123.785. The average for this metric is 118.554.", "is_anomalous": false}, {"value": 128.475, "average": 119.076578947, "min_value": 104.811357207, "max_value": 133.341800688, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "6fe89201f166fda90aac81ed7d8c1247", "metric_id": "e27c9b83e3fc27c56ad071e8a5110cde", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "average", "anomaly_score": 1.976503672, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 128.475, "min_metric_value": 104.811357207, "max_metric_value": 133.341800688, "training_avg": 119.076578947, "training_stddev": 4.755073914, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last average value is 128.475. The average for this metric is 119.077.", "is_anomalous": false}, {"value": 120.25, "average": 119.13525, "min_value": 105.22820793, "max_value": 133.04229207, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "45f1176b4170c4485fc6510c37893341", "metric_id": "c3f3df9a76f066888439b077a428cb15", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "average", "anomaly_score": 0.2404716965, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 120.25, "min_metric_value": 105.22820793, "max_metric_value": 133.04229207, "training_avg": 119.13525, "training_stddev": 4.63568069, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last average value is 120.25. The average for this metric is 119.135.", "is_anomalous": false}, {"value": 127.37, "average": 119.527380952, "min_value": 104.939802969, "max_value": 134.114958936, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "3b950410fcaf8c1d4672b762d3056e93", "metric_id": "1a7815168af68667c7fc99cab3770298", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "average", "anomaly_score": 1.612869331, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 127.37, "min_metric_value": 104.939802969, "max_metric_value": 134.114958936, "training_avg": 119.527380952, "training_stddev": 4.862525995, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last average value is 127.37. The average for this metric is 119.527.", "is_anomalous": false}, {"value": 118.67, "average": 119.488409091, "min_value": 105.24183252, "max_value": 133.734985662, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "d82e677625b96ba2c60ed09420e37b70", "metric_id": "a8e19bc72debe1b82c12c16c83a4cc93", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "average", "anomaly_score": -0.1723380533, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 118.67, "min_metric_value": 105.24183252, "max_metric_value": 133.734985662, "training_avg": 119.488409091, "training_stddev": 4.748858857, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last average value is 118.67. The average for this metric is 119.488.", "is_anomalous": false}, {"value": 126.37, "average": 119.787608696, "min_value": 105.218120433, "max_value": 134.357096958, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "79ed24c91a08740802816e9370a5fee0", "metric_id": "2466c716fb9da3a1c4abfe33f80a8b75", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "average", "anomaly_score": 1.35537869, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 126.37, "min_metric_value": 105.218120433, "max_metric_value": 134.357096958, "training_avg": 119.787608696, "training_stddev": 4.856496088, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last average value is 126.37. The average for this metric is 119.788.", "is_anomalous": false}, {"value": 120.715, "average": 119.82625, "min_value": 105.565696757, "max_value": 134.086803243, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "21ec4b10018f3d35e337da10803324a9", "metric_id": "cd0b9a8ab3a054580209aeddd5163640", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "average", "anomaly_score": 0.1869667996, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 120.715, "min_metric_value": 105.565696757, "max_metric_value": 134.086803243, "training_avg": 119.82625, "training_stddev": 4.753517748, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last average value is 120.715. The average for this metric is 119.826.", "is_anomalous": false}, {"value": 121.75, "average": 119.9032, "min_value": 105.895266614, "max_value": 133.911133386, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "86b16e34e1d5c870027c2ee54ea7b26f", "metric_id": "8dcafc31b4292226215f2649441c5fda", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "average", "anomaly_score": 0.3955187284, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 121.75, "min_metric_value": 105.895266614, "max_metric_value": 133.911133386, "training_avg": 119.9032, "training_stddev": 4.669311129, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last average value is 121.75. The average for this metric is 119.903.", "is_anomalous": false}, {"value": 116.98, "average": 119.790769231, "min_value": 105.958516035, "max_value": 133.623022426, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "df94bfb35c16cc2b1ed235f43f449ee4", "metric_id": "978b5cde2206faaaa99e3bdb23800e37", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "average", "anomaly_score": -0.6096120114, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 116.98, "min_metric_value": 105.958516035, "max_metric_value": 133.623022426, "training_avg": 119.790769231, "training_stddev": 4.610751065, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last average value is 116.98. The average for this metric is 119.791.", "is_anomalous": false}, {"value": 118.48, "average": 119.742222222, "min_value": 106.157486657, "max_value": 133.326957788, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "c62e8cdde8a075015de78d8ed026b76c", "metric_id": "80ec2565d7f0e5f532508d99c4c0b788", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "average", "anomaly_score": -0.2787442309, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 118.48, "min_metric_value": 106.157486657, "max_metric_value": 133.326957788, "training_avg": 119.742222222, "training_stddev": 4.528245189, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last average value is 118.48. The average for this metric is 119.742.", "is_anomalous": false}, {"value": 129.135, "average": 120.077678571, "min_value": 105.722615305, "max_value": 134.432741837, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "ea1c1f80e3a60939848c4bae364fbff3", "metric_id": "c88078f208e2dcffd4f7863fdc6b15e5", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "average", "anomaly_score": 1.892848801, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 129.135, "min_metric_value": 105.722615305, "max_metric_value": 134.432741837, "training_avg": 120.077678571, "training_stddev": 4.785021089, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last average value is 129.135. The average for this metric is 120.078.", "is_anomalous": false}, {"value": 113.94, "average": 119.866034483, "min_value": 105.36088691, "max_value": 134.371182055, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "b2a56145d95c04837aa4a240997075bf", "metric_id": "54e2e4b9b8b4248933a099d618cf2487", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "average", "anomaly_score": -1.225640991, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 113.94, "min_metric_value": 105.36088691, "max_metric_value": 134.371182055, "training_avg": 119.866034483, "training_stddev": 4.835049191, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last average value is 113.94. The average for this metric is 119.866.", "is_anomalous": false}, {"value": 59.135, "average": 117.841666667, "min_value": 105.36088691, "max_value": 134.371182055, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "b55cc8424053f299a37b257ec8511e98", "metric_id": "102f8bd5e0cc6b31e570ce90c77af5f0", "test_execution_id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "detected_at": "2023-01-02T10:44:23.901000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES", "column_name": "ZERO_PERCENT", "metric_name": "average", "anomaly_score": -4.866713067, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 59.135, "min_metric_value": 81.652970608, "max_metric_value": 154.030362725, "training_avg": 117.841666667, "training_stddev": 12.062898686, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column ZERO_PERCENT, the last average value is 59.135. The average for this metric is 117.842.", "is_anomalous": true}], "result_description": "In column ZERO_PERCENT, the last average value is 59.135. The average for this metric is 117.842."}}], "model.elementary_integration_tests.any_type_column_anomalies": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "average", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_INT, the last average value is 151.433. The average for this metric is 149.992.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Average", "metrics": [{"value": 149.491981672, "average": 149.204753723, "min_value": 147.986148737, "max_value": 150.423358708, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "12dfc3ae988d36b28920f8473a001a45", "metric_id": "11601b3430642d8c5e855b3b8f9adc17", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "average", "anomaly_score": 0.7071067812, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 149.491981672, "min_metric_value": 147.986148737, "max_metric_value": 150.423358708, "training_avg": 149.204753723, "training_stddev": 0.4062016618, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last average value is 149.492. The average for this metric is 149.205.", "is_anomalous": false}, {"value": 150.811569301, "average": 149.740358916, "min_value": 146.826930014, "max_value": 152.653787817, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "49ecf5dd723b526cc999a88d1e5f246c", "metric_id": "363c5267968601cbdb32de5e8eb387bc", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "average", "anomaly_score": 1.103040872, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 150.811569301, "min_metric_value": 146.826930014, "max_metric_value": 152.653787817, "training_avg": 149.740358916, "training_stddev": 0.9711429672, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last average value is 150.812. The average for this metric is 149.74.", "is_anomalous": false}, {"value": 149.206185567, "average": 149.606815578, "min_value": 147.096690163, "max_value": 152.116940994, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "f7032dd5e00c30afafc32e681e04d8bb", "metric_id": "ab133c2ed1d84c735624f7e7439d2a55", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "average", "anomaly_score": -0.4788167264, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 149.206185567, "min_metric_value": 147.096690163, "max_metric_value": 152.116940994, "training_avg": 149.606815578, "training_stddev": 0.8367084718, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last average value is 149.206. The average for this metric is 149.607.", "is_anomalous": false}, {"value": 149.126575029, "average": 149.510767468, "min_value": 147.24346004, "max_value": 151.778074897, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "5356c13dcd5206bb201b4eab0a322d27", "metric_id": "f8c4b99a7f6a11b089cd4587878f6f09", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "average", "anomaly_score": -0.50834629, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 149.126575029, "min_metric_value": 147.24346004, "max_metric_value": 151.778074897, "training_avg": 149.510767468, "training_stddev": 0.7557691428, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last average value is 149.127. The average for this metric is 149.511.", "is_anomalous": false}, {"value": 149.127147766, "average": 149.446830851, "min_value": 147.365174661, "max_value": 151.528487042, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "88f09f8353186678a38821e6b18d9e3d", "metric_id": "658a6a891f0778a62652c9a54de804a2", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "average", "anomaly_score": -0.4607145311, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 149.127147766, "min_metric_value": 147.365174661, "max_metric_value": 151.528487042, "training_avg": 149.446830851, "training_stddev": 0.6938853967, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last average value is 149.127. The average for this metric is 149.447.", "is_anomalous": false}, {"value": 149.802405498, "average": 149.49762723, "min_value": 147.555042813, "max_value": 151.440211646, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "681eb81f3bcdf615235884c485382225", "metric_id": "f8129202176940c63b997429a40b32c9", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "average", "anomaly_score": 0.4706795742, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 149.802405498, "min_metric_value": 147.555042813, "max_metric_value": 151.440211646, "training_avg": 149.49762723, "training_stddev": 0.6475281389, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last average value is 149.802. The average for this metric is 149.498.", "is_anomalous": false}, {"value": 151.176403207, "average": 149.707474227, "min_value": 147.176640669, "max_value": 152.238307784, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "81b8d0934f9cff2092436cdd8d0ec22d", "metric_id": "78e48893f279ca26464eed125a24f004", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "average", "anomaly_score": 1.741239335, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 151.176403207, "min_metric_value": 147.176640669, "max_metric_value": 152.238307784, "training_avg": 149.707474227, "training_stddev": 0.8436111858, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last average value is 151.176. The average for this metric is 149.707.", "is_anomalous": false}, {"value": 150.528636884, "average": 149.798714522, "min_value": 147.292963778, "max_value": 152.304465267, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "29331f7a95e0ba37693376c0d73800d4", "metric_id": "cc82fbe3e699b95eae40fd372a57c576", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "average", "anomaly_score": 0.8738966122, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 150.528636884, "min_metric_value": 147.292963778, "max_metric_value": 152.304465267, "training_avg": 149.798714522, "training_stddev": 0.8352502482, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last average value is 150.529. The average for this metric is 149.799.", "is_anomalous": false}, {"value": 149.487399771, "average": 149.767583047, "min_value": 147.386749369, "max_value": 152.148416725, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "d048372f74ff985f8f0c14c27880f045", "metric_id": "9744f2d2dfafdbb08a2583c7be1ea6d6", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "average", "anomaly_score": -0.3530485291, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 149.487399771, "min_metric_value": 147.386749369, "max_metric_value": 152.148416725, "training_avg": 149.767583047, "training_stddev": 0.7936112261, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last average value is 149.487. The average for this metric is 149.768.", "is_anomalous": false}, {"value": 150.107674685, "average": 149.798500469, "min_value": 147.518990672, "max_value": 152.078010265, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "aab8de67eaddd1ebb6837c21338e68f0", "metric_id": "6fdabf915d2be829c3d6c81fc46d5fe6", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "average", "anomaly_score": 0.4068956627, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 150.107674685, "min_metric_value": 147.518990672, "max_metric_value": 152.078010265, "training_avg": 149.798500469, "training_stddev": 0.7598365988, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last average value is 150.108. The average for this metric is 149.799.", "is_anomalous": false}, {"value": 150.857388316, "average": 149.886741123, "min_value": 147.527775483, "max_value": 152.245706762, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "db0567170d75daeacc7ce6d099910ebd", "metric_id": "2bc7154a7ce16b79a4b038e9c738e37f", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "average", "anomaly_score": 1.234414581, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 150.857388316, "min_metric_value": 147.527775483, "max_metric_value": 152.245706762, "training_avg": 149.886741123, "training_stddev": 0.7863218799, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last average value is 150.857. The average for this metric is 149.887.", "is_anomalous": false}, {"value": 150.83906071, "average": 149.959996475, "min_value": 147.566493837, "max_value": 152.353499114, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "d0d07ae3e78f982fb15d1adfca388f0c", "metric_id": "43c5085d809e7652544abe7095160359", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "average", "anomaly_score": 1.101813159, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 150.83906071, "min_metric_value": 147.566493837, "max_metric_value": 152.353499114, "training_avg": 149.959996475, "training_stddev": 0.7978342129, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last average value is 150.839. The average for this metric is 149.96.", "is_anomalous": false}, {"value": 150.001718213, "average": 149.9629766, "min_value": 147.663130352, "max_value": 152.262822847, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "c269b4d39cd35b28481306759f2f023c", "metric_id": "3d29eb47439d27cbddfa411d2638fd86", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "average", "anomaly_score": 0.0505359176, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 150.001718213, "min_metric_value": 147.663130352, "max_metric_value": 152.262822847, "training_avg": 149.9629766, "training_stddev": 0.7666154157, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last average value is 150.002. The average for this metric is 149.963.", "is_anomalous": false}, {"value": 150.147766323, "average": 149.975295914, "min_value": 147.754490997, "max_value": 152.196100832, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "1e241e8ef53e9ec4548cc0b8951cf164", "metric_id": "3429a2c005bec7112eec10955be9f665", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "average", "anomaly_score": 0.2329836455, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 150.147766323, "min_metric_value": 147.754490997, "max_metric_value": 152.196100832, "training_avg": 149.975295914, "training_stddev": 0.7402683057, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last average value is 150.148. The average for this metric is 149.975.", "is_anomalous": false}, {"value": 151.23997709, "average": 150.054338488, "min_value": 147.708523262, "max_value": 152.400153714, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "396316557ee2ab07bfa6e71298e7cf1f", "metric_id": "e39176548f7ce5e2a80bb9b5fc601a9b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "average", "anomaly_score": 1.51628132, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 151.23997709, "min_metric_value": 147.708523262, "max_metric_value": 152.400153714, "training_avg": 150.054338488, "training_stddev": 0.7819384087, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last average value is 151.24. The average for this metric is 150.054.", "is_anomalous": false}, {"value": 149.400343643, "average": 150.015868203, "min_value": 147.695231368, "max_value": 152.336505038, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "43cdcaeb7166b86ae2ea40bf63de7c40", "metric_id": "006f5eb33b1b940be7217f367b48d0f6", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "average", "anomaly_score": -0.7957185084, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 149.400343643, "min_metric_value": 147.695231368, "max_metric_value": 152.336505038, "training_avg": 150.015868203, "training_stddev": 0.7735456117, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last average value is 149.4. The average for this metric is 150.016.", "is_anomalous": false}, {"value": 149.406071019, "average": 149.981990582, "min_value": 147.689721833, "max_value": 152.27425933, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "24b328b7ffd750943970c9b91da449f0", "metric_id": "1fb2fd654c9e31cd61714faa23b64f0a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "average", "anomaly_score": -0.7537330374, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 149.406071019, "min_metric_value": 147.689721833, "max_metric_value": 152.27425933, "training_avg": 149.981990582, "training_stddev": 0.7640895829, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last average value is 149.406. The average for this metric is 149.982.", "is_anomalous": false}, {"value": 151.350515464, "average": 150.054018207, "min_value": 147.63539795, "max_value": 152.472638464, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "ca8643b0e8712bb730aca3a64438b303", "metric_id": "f62f06554e382d4a83d3f3b9ab2c88f0", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "average", "anomaly_score": 1.608144875, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 151.350515464, "min_metric_value": 147.63539795, "max_metric_value": 152.472638464, "training_avg": 150.054018207, "training_stddev": 0.8062067524, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last average value is 151.351. The average for this metric is 150.054.", "is_anomalous": false}, {"value": 149.398052692, "average": 150.021219931, "min_value": 147.626334844, "max_value": 152.416105019, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "1757dff471458e06b9ae976667c8977b", "metric_id": "3f5dad74c06548e0add3f4b292992cc1", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "average", "anomaly_score": -0.780622723, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 149.398052692, "min_metric_value": 147.626334844, "max_metric_value": 152.416105019, "training_avg": 150.021219931, "training_stddev": 0.7982950291, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last average value is 149.398. The average for this metric is 150.021.", "is_anomalous": false}, {"value": 150.18556701, "average": 150.029045983, "min_value": 147.692322518, "max_value": 152.365769447, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "32c558c10089c3b4f27023f35e4aeb87", "metric_id": "bd003f93ea0185d9541dcc418cb4b875", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "average", "anomaly_score": 0.2009493592, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 150.18556701, "min_metric_value": 147.692322518, "max_metric_value": 152.365769447, "training_avg": 150.029045983, "training_stddev": 0.7789078216, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last average value is 150.186. The average for this metric is 150.029.", "is_anomalous": false}, {"value": 150.087628866, "average": 150.031708841, "min_value": 147.750992427, "max_value": 152.312425255, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "a6a588fa7804a0b4770c8f1963dbe0c3", "metric_id": "f55d8def1c9d465dc0ea264fcb98be87", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "average", "anomaly_score": 0.07355586776, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 150.087628866, "min_metric_value": 147.750992427, "max_metric_value": 152.312425255, "training_avg": 150.031708841, "training_stddev": 0.7602388048, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last average value is 150.088. The average for this metric is 150.032.", "is_anomalous": false}, {"value": 150.407216495, "average": 150.048035261, "min_value": 147.807409411, "max_value": 152.28866111, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "bf27ae693628864dc9b7a3eea1700761", "metric_id": "0091707258c3ec732cbe4f2b7d5378fd", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "average", "anomaly_score": 0.4809119303, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 150.407216495, "min_metric_value": 147.807409411, "max_metric_value": 152.28866111, "training_avg": 150.048035261, "training_stddev": 0.7468752832, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last average value is 150.407. The average for this metric is 150.048.", "is_anomalous": false}, {"value": 149.157502864, "average": 150.010929744, "min_value": 147.752718342, "max_value": 152.269141146, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "6119d4705df762673664e94335cb209a", "metric_id": "7e82512f90aae8affaaf8c272fed83a7", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "average", "anomaly_score": -1.133764819, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 149.157502864, "min_metric_value": 147.752718342, "max_metric_value": 152.269141146, "training_avg": 150.010929744, "training_stddev": 0.7527371339, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last average value is 149.158. The average for this metric is 150.011.", "is_anomalous": false}, {"value": 149.729667812, "average": 149.999679267, "min_value": 147.782582569, "max_value": 152.216775965, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "1283af5c040f314b5a53aa6707be544d", "metric_id": "c1d64bc05958b8051c388765e8f64d2b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "average", "anomaly_score": -0.3653581574, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 149.729667812, "min_metric_value": 147.782582569, "max_metric_value": 152.216775965, "training_avg": 149.999679267, "training_stddev": 0.7390322326, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last average value is 149.73. The average for this metric is 150.", "is_anomalous": false}, {"value": 150.068728522, "average": 150.002335007, "min_value": 147.829652921, "max_value": 152.175017094, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "4f51a32fef191c8408e38da270ad6235", "metric_id": "77918a356e57faba448c062ad63fbfb2", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "average", "anomaly_score": 0.09167496055, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 150.068728522, "min_metric_value": 147.829652921, "max_metric_value": 152.175017094, "training_avg": 150.002335007, "training_stddev": 0.7242273621, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last average value is 150.069. The average for this metric is 150.002.", "is_anomalous": false}, {"value": 149.302405498, "average": 149.976411692, "min_value": 147.807935644, "max_value": 152.14488774, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "c7e8e58cfbf9ed1292e4950d2779ae17", "metric_id": "adbed14ce98aa08f4eec619b2ce3af64", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "average", "anomaly_score": -0.9324606486, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 149.302405498, "min_metric_value": 147.807935644, "max_metric_value": 152.14488774, "training_avg": 149.976411692, "training_stddev": 0.7228253493, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last average value is 149.302. The average for this metric is 149.976.", "is_anomalous": false}, {"value": 150.044100802, "average": 149.978829161, "min_value": 147.850542933, "max_value": 152.107115389, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "fa17c1d4d19a08a8e3c3d458a4ed06f7", "metric_id": "8c8e431900bbc4fd9c7e83bd3b252dd4", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "average", "anomaly_score": 0.092005916, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 150.044100802, "min_metric_value": 147.850542933, "max_metric_value": 152.107115389, "training_avg": 149.978829161, "training_stddev": 0.7094287427, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last average value is 150.044. The average for this metric is 149.979.", "is_anomalous": false}, {"value": 148.914089347, "average": 149.942113995, "min_value": 147.769636227, "max_value": 152.114591762, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "ae045616c0af772280877095f233e8d4", "metric_id": "f170201bcc5fcc3a581e4abfe382bdb0", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "average", "anomaly_score": -1.419611279, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 148.914089347, "min_metric_value": 147.769636227, "max_metric_value": 152.114591762, "training_avg": 149.942113995, "training_stddev": 0.7241592558, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last average value is 148.914. The average for this metric is 149.942.", "is_anomalous": false}, {"value": 151.433333333, "average": 149.991821306, "min_value": 147.706206881, "max_value": 152.277435731, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "b7d9c8657cd9e2246cdb2632d322f4f1", "metric_id": "8badac87d7153c2e0e4ddc14166b0833", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "average", "anomaly_score": 1.892067199, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 151.433333333, "min_metric_value": 147.706206881, "max_metric_value": 152.277435731, "training_avg": 149.991821306, "training_stddev": 0.761871475, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last average value is 151.433. The average for this metric is 149.992.", "is_anomalous": false}], "result_description": "In column NULL_COUNT_INT, the last average value is 151.433. The average for this metric is 149.992."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_FLOAT, the last null_count value is 240. The average for this metric is 58.7.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Null Count", "metrics": [{"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "c9d245838728fdf4c3ee79f0914a03a9", "metric_id": "624e1430c46c787ebfa1c3701a649e83", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "59028c00e217a732468a2e88a0bf41fd", "metric_id": "9aa7ce15480241f0112832b4a3b884c3", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "18ccc595cdcc2f14a551fc322c33bf5f", "metric_id": "661dfe0345fa14bf44970fe1414aaf93", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "0ef7efee1ec8964829766156c899a2a6", "metric_id": "9d399c89ccec142b3d9da2af91adb08e", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "43d8e70c0cf75d9965c9db3e1830d893", "metric_id": "027cb23ad2249dc7782aa562b2045d1b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "0196c1650905854bb57880170101bb69", "metric_id": "ae7e6fa3ff697345eb324b5481b6c489", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "13b1a6b0e598f9a3f85984da2458e50a", "metric_id": "2c350f530f61c2f13aec9d8683f7463c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "cfe9eb2993fbdbf2a29a2f81883369d3", "metric_id": "aa891c665b286dab421bee95a1151054", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "5bcee21732ce9895dc486a50b5b97ac0", "metric_id": "e6779ba2cd2ab6476fe3541a93200b79", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "68a35998a0ccb0235081cc4ff521c9a3", "metric_id": "feedd934702696d986ef6bd8ff372f41", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "cf87d0bf78b88d4fe4ec5804ef11e91f", "metric_id": "288457732704d3d891f26393b387b299", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "82e4484dd5e615aa30f9cbf1481eaff6", "metric_id": "3e166d3a66f79efb4ba4988fefc6099c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "8751da155a92f51a92b915bfcebce66b", "metric_id": "e5926eb45c0522d2e830343705132b2a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "f78b592b5d603eb43365b0c7ef361879", "metric_id": "df5488b37a0eb4fd99e6f5a239cd1759", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "243d9012e0f50272eaaca187b8d81ca1", "metric_id": "e7603634075d19a487b15b9066e0f303", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "d5d707175bcec337b253f651908f3cda", "metric_id": "b8e41b991fc8207603801f042be49266", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "77a1581be58d765b4f21a43455a2ab6d", "metric_id": "a143f283372e442f481ff8cc04a25d38", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "5486ac685e1e57a36d060c6dd83b1910", "metric_id": "930d0c61fdbfadbd15efdf29591bb23a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "a61896335a9b52f72c8a4e1b6b7e41f5", "metric_id": "39a3d7f4226ad077d7e371fc4c2d1f14", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "6e404ae31d37db083dc1fe392c022283", "metric_id": "350acd116e79ad9eca3bb98ce00665b1", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "c4ab311771731d2da95181a598e20ba3", "metric_id": "3741b045529da4c3dc20791cf46ee337", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "a3468fefb0cd38f1eea9025f3552250e", "metric_id": "7d6d6c5bed3df907a6202513522ac039", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "4417afe0a993ddf11eb0d77fac897e37", "metric_id": "52a451d1b5c2e2acddb3ef1e3bc1c665", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "504c90717a87d487c2af888d538a34c2", "metric_id": "763adb3b3246f466441274b9dca41141", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "70b44a104b72564c382bbbaffd0dd9b8", "metric_id": "a3d1a4c6e3f329c9c0d0d57117e5857c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "b4c7afb3d65d88cee84c85def83d21d1", "metric_id": "d0e7abee7c1ed9ea8742009fc5ec9416", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "d114d1d71cc39fa0c55006fad30e5272", "metric_id": "a3a88b880659a9a010a2b1cf70bc2360", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 9.0, "average": 52.448275862, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "b6fedc0dea38be8fe0a7bc348e7dbdab", "metric_id": "981a7438d065c0996209c97bdf017c70", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_count", "anomaly_score": -5.199469469, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 9.0, "min_metric_value": 27.379405208, "max_metric_value": 77.517146516, "training_avg": 52.448275862, "training_stddev": 8.356290218, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_count value is 9. The average for this metric is 52.448.", "is_anomalous": true}, {"value": 240.0, "average": 58.7, "min_value": 27.379405208, "max_value": 77.517146516, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "eddce3e548c02d9f02f91604a0e7772a", "metric_id": "27dfa80cc395ca89255d4aed9e135dd9", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_count", "anomaly_score": 5.148695731, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 240.0, "min_metric_value": -46.938404067, "max_metric_value": 164.338404067, "training_avg": 58.7, "training_stddev": 35.212801356, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_count value is 240. The average for this metric is 58.7.", "is_anomalous": true}], "result_description": "In column NULL_COUNT_FLOAT, the last null_count value is 240. The average for this metric is 58.7."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_INT, the last null_percent value is 80. The average for this metric is 5.567.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Null Percent", "metrics": [{"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "d33c36ee6c8eaf59d35508679e407a8e", "metric_id": "3d28f12047be4e28e267e467bd12e9ea", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "e0ede3a4f9bd59202fbf1bd5a03e0b44", "metric_id": "04bdbf6ae95783dd0381c7b413a4df2c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "8ed3f37930f0805890da0050626a3940", "metric_id": "ba07b5ae7955b2b04944281ce6526e67", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "249e8e92826cd734c8234e7bbb2553c1", "metric_id": "d36691e7a74968560606161ab65282dc", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "29a20d72cad227b3a402d097d4b1f3b8", "metric_id": "a368c62765e5c69a19c7b029dfae00ee", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "de733f907b1d19e22ec95bfb7390060c", "metric_id": "9c63bf5aeb5cb1c7c1b431d7cf3820c2", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "f507bd33162b5ba055072ae1513b6aa5", "metric_id": "8e2af3ac5c5c7f0c90fe628174aa6edc", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "8da802e1ed5504b0b3b779bb15812207", "metric_id": "11dc475d29be5e71a50fc36ff5352338", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "fb1a10d9bd7390f42b6707582c0310fb", "metric_id": "d258c5282f954a0f7389b16f947e2a28", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "a45170618444eee5f0787f5dc530a18a", "metric_id": "4c815645208df945ad897aadccbe1aa6", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "2a0898679e19cbe4d2d1d928c171a82c", "metric_id": "b606701a46134193c7a43d3224c0c00d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "9ada6da30b2177735a6415adde4dd0fc", "metric_id": "487d4066ddeeca54c18b55dc58b653c9", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "6233d95fe43fac174134a0a0af5f2d51", "metric_id": "f2d84ec90cff64fa52e95d2de2ba054d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "af0ab791d92a520f6aa6010d2c32169f", "metric_id": "288ef01507235891ade48bc9d6de0885", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "ab3c4e397c5b5f094410b7dab5ea4009", "metric_id": "530910928bd396cafa5facca2b7574bc", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "21fbb5e3826575ec030150168ddea926", "metric_id": "d2f03b47c98ec43f7a156fe401fa4a1b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "1ea2b6a51c1b0c4cace16f7400fdc10c", "metric_id": "155aef7000023ccc80713680b9c82c10", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "ceb057d91f90eecb7b7d2f3eb7aa75e8", "metric_id": "d3aae6c7f368d1c315e96d0692400f86", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "a5079cede1ec1e8efb2eeaf64e892ed6", "metric_id": "242d23619f61ce0c1c772dc16cea39f0", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "6c65f6c4ea82e366858aea18bbed35da", "metric_id": "b7587c5e4d51ad11aeb113bce3b2a104", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "c2707df350fad18faedc78e6afac2c49", "metric_id": "1de8bdefedf61a10246b6af1f7bc9d35", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "90414ca7bba6698509c4439ae758f1e6", "metric_id": "cba2686e1bde1e8bf894e27e98d2db9a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "a3fefeb65c7ece635fd6b862894ec3da", "metric_id": "f05adade961604f9abf472ea1c97eb8b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "d2758ee33465d97d67a7664e3594804a", "metric_id": "60fe391e9591cc3e222f308b3a814719", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "8ec892456967a3628935c61b58ad2032", "metric_id": "073776588e995e8ac5e1c6b4b2a9aa82", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "77070d1ed46a117ff6c0999c32d9250c", "metric_id": "1091e695db80c2266569639173186ec0", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "effcac9dd4fcbd88779f4e562989fc99", "metric_id": "dc919480d23342eac90d5aefb156f4b8", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "a58384a76127cc7b93c869bb2d34e958", "metric_id": "ec8c7df724b283099f047a0d5c014852", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 80.0, "average": 5.566666667, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "b3f9478b02b0683f7d233e6b15348315", "metric_id": "134172964e30dd4c4e99c0c4b7bdc011", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_percent", "anomaly_score": 5.294651389, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 80.0, "min_metric_value": -36.607970261, "max_metric_value": 47.741303595, "training_avg": 5.566666667, "training_stddev": 14.058212309, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_percent value is 80. The average for this metric is 5.567.", "is_anomalous": true}], "result_description": "In column NULL_COUNT_INT, the last null_percent value is 80. The average for this metric is 5.567."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "UPDATED_AT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('UPDATED_AT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column UPDATED_AT, the last null_count value is 0. The average for this metric is 0.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('UPDATED_AT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Null Count", "metrics": [{"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "89a207e275d3b46f7cf5ae0992d24b17", "metric_id": "779fb63af7a46752103f30d8e2798217", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "2e2caa6de85b10ba5f35f224cd75bae7", "metric_id": "c2efa16fc351637a8822bab34ca37242", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "ad40b6eb568a2e8c56aa8b44fc2d82eb", "metric_id": "689b7978305077654cf5c8edbe9296c7", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "3cf266c3670b4a25914f5114ce54ccd4", "metric_id": "8de44dfa7c1c8863122506e96bd8dabe", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "a27eb91e4333c101f95eef82248361b1", "metric_id": "cb2ffe9dac1a931dbfe93d8ffd2a57a3", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "0044595df1477c03b0e17138dc18946d", "metric_id": "119ed187b22910a4d93493d9acd7253b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "e98843fc2c2cbf943e0aa0af3bde4988", "metric_id": "5e176a97cd025f02091ccd6893eb27b7", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "2ca445a58f2e3c6af7c97559f1a3700f", "metric_id": "89c0ef52895dc7d53d18d5b5fb61f2e3", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "e4b1f7f874469ee2d95833a21eb19b11", "metric_id": "aed6e3f4e5519da92e653ce04c3e13de", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "73336094df60597826530336087742cb", "metric_id": "19b99da9bb619758ebb39c06a21834e0", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "a41aff6495dc53c2d8be8629c0e64ccc", "metric_id": "7988ff80f1e31c15e34f5594b42c173e", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "e7919a954ace495b2ee04088f479abac", "metric_id": "4391052256c9f786aed61a96c581bce9", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "ca005cfd9399f304aadbfdb4d85c0512", "metric_id": "e89904bff4586db1a0d90723f7650351", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "ccb54eb0510dc54d033828007a59f6ba", "metric_id": "2c5cddb3cbc7e41319860b334c41730b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "c65de92a79a39293637cad1bdcfdab96", "metric_id": "0e0aec3641210b6255fdf35a765045bd", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "3dbab707835ec1ec8e4b4b0e2db6d484", "metric_id": "d225b11f2e2ff661e6c6d800d450a424", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "3ddcb57c3196a665518c5dd72be6379e", "metric_id": "c281b9e517e5290af3ffd7a0ec8fe6c3", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "4c8bc938c811ec0c0e5e356432c39837", "metric_id": "ba3a093214ff3bfd1115bb719cbee05f", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "b0653b1a0c94d737567ed59998fb8acf", "metric_id": "9170501fdee31b17bca9205a00f13c97", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "72e5fa16e028b5a1986788f560de9d79", "metric_id": "18a1936d36345f5c56c1eb8d3cadb0f6", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "a6271f9fb533da320a3627642cd55ff0", "metric_id": "f91e8d7f0db4c186cedd6dfe8e5a6bf5", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "78aa2b24f57c04db87c469976bf3f749", "metric_id": "b30839bb7bcebc1fe2ca4333a6aab1d4", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "f5bde719f3b2da06492dbee20ebb594f", "metric_id": "9e28c61c96206a9c66b8cd524373a5e5", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "3f9347533d59331d13db4917e9c7bd35", "metric_id": "09cad986ea7ccdeb81ca4a7ae9b13e3e", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "c4ca0ab40cb1692afa9720f0d804203d", "metric_id": "3dbc72e643d69868b8c3efca7466f814", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "4ceca4201fcdc8bd52b43233164fd8d3", "metric_id": "bc6c19239a1a20705c7077cc4c8bdf70", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "3d31953b3ca75e57967a29ee55b0361c", "metric_id": "6894525f6ee13e8959ab56f533b68838", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "523e19dd9e7d59ec2d0fdb439e9bbe8d", "metric_id": "592357250a0721f3116686ba6e49b4ea", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "39d5f5ab5711dff43c925a8edf0e1ca1", "metric_id": "672209f1e2966d5348319057f645219f", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}], "result_description": "In column UPDATED_AT, the last null_count value is 0. The average for this metric is 0."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "min_length", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('min_length' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_STR, the last min_length value is 5. The average for this metric is 5.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('min_length' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Min Length", "metrics": [{"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "58d3bee5ab7132041bd6a8eef4fb1110", "metric_id": "4abd2ab4ae3ba482f571289da768aa16", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "d1ccc06d32045d0ebfed792a78809fc8", "metric_id": "1a7d8444a9884dd86b7111192dc574ad", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "7e7f8c90fd39ae61218543419216576f", "metric_id": "e5f52e91e3e505c7f1fc4eed672b43e7", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "bd16ea3cc7b4b71978aa92665f4b3f4b", "metric_id": "7c19c8e6231bd70520b15c8c8ba91a94", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "6935de5bc1a36c1f6bd7dbd6465dc291", "metric_id": "f715d44df7501f3a4fd0fa2d76898aed", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "e05715632078cec87c68d206476d4d5c", "metric_id": "d2e489c96fdcac3ffc14ad4e310748f5", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "717322b2a2f156ef334edb798cd48a4f", "metric_id": "562013637d1a5c6ffa136faf504d632c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "70fb4cd064e0aa1bb8a71cd4584ad8ef", "metric_id": "c82a4a122fb0cede26f9e557bf953b8b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "398997209002a4f3e387cba73d3787f2", "metric_id": "937e6e55ccce8ab1c7dae31ca0db95ba", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "d32cc14a5bc6751f7bd2b7fffaeabf16", "metric_id": "2cd05a6b6eec1a7618115d8b6d88ee89", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "3eb63e922ac0ac8410bcd432d1a48167", "metric_id": "573503e7945d3630f736321b99cfb589", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "94e7e15eadd62c6fafa68c73e025b3fc", "metric_id": "989eb91e1a1c72a8316511b8eb133c30", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "953ff079afdc403cc174c39387539f9f", "metric_id": "8c878fc9804f86fcc7dd5f11f137222a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "3449454388b58f12a2413d2b90c6fe6e", "metric_id": "6781abd3f9a054821c31fbc0fdd42bc3", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "56a608cca6abaf3b87b55ec960ad40f7", "metric_id": "412fbc851445e5a76205413df6f37433", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "4ddbba0f6c28d2256f3a091a9575b603", "metric_id": "9860b0f90472cc798319acf038f5967f", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "a59c01cf0e45cf6077e3216d6932551b", "metric_id": "d82bf9eabd003e010f9b642d2cfa4ecb", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "4dd649f7d13d5fde041a1a43d9f17dc7", "metric_id": "f769502d700c1d90d0202bacfaf222ac", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "e705cd801d26a7456bc2474a338cedfa", "metric_id": "dcd8157f96c756f3f2985df92b2257a5", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "8507f99b47d040c51806de6b7fafd164", "metric_id": "b14a6a5ac8b129d3145ecac0709c1cbd", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "1e6cdc88251c1612b916e3e126c4a0bf", "metric_id": "ce3096bfa031fdb13e5ae7a5e0a0f06e", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "1531e2b58190de118709c0f1f8120e4a", "metric_id": "72523a8a3b1414c2ee1bdcd8f4980811", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "c3df2da410b1ae6402c9db747a75679f", "metric_id": "4c771cdf47de5d7b6e96105765de604d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "1f528ab8a63ad37d26be06081e331c1f", "metric_id": "19c454906852ea6ba0facc780c2de018", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "0dddedeb97b9a4565f37b5bd3eb401ef", "metric_id": "764c3f11c670d0a6fbb9ec7518e67e8d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "b17b4c9d487e01bb0e0ebc33ce48820d", "metric_id": "a710e368ac8abdda58f795916309babf", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "1df666cf4a2ea6bf2826258423fb2f41", "metric_id": "980af6cb3787aee8a69bcb85c20b640f", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "ecb219f858cf427cbbcd623125c7965d", "metric_id": "6891b150a3dec98765dfffa01f3ae9c8", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "466ad4714a814066bc3e2d3cff48aca7", "metric_id": "96b6835ecef5ff1f4df07ed23cc4619f", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}], "result_description": "In column NULL_COUNT_STR, the last min_length value is 5. The average for this metric is 5."}}, {"metadata": {"test_unique_id": "model.elementary_integration_tests.any_type_column_anomalies.elementary_volume_anomalies_any_type_column_anomalies_hour__4.volume_anomalies", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": null, "test_name": "volume_anomalies", "test_display_name": "Volume Anomalies", "latest_run_time": "2023-01-02T12:42:20+02:00", "latest_run_time_utc": "2023-01-02T10:42:20+00:00", "latest_run_status": "warn", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "row_count", "test_query": "select * from (\n \n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_VOLUME_ANOMALIES_ANY_TYPE_COLUMN_ANOMALIES_HOUR__4__ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n\n \n\n\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('row_count' as varchar)", "test_params": {}, "test_created_at": null, "description": "This is a very weird description with breaklines and comma, and even a string like this 'wow'. You know, these $##$34#@#!^ can also be helpful WDYT?\n", "result": {"result_description": "The last row_count value is 0. The average for this metric is 281.667.", "result_query": "select * from (\n \n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_VOLUME_ANOMALIES_ANY_TYPE_COLUMN_ANOMALIES_HOUR__4__ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n\n \n\n\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('row_count' as varchar)"}, "configuration": {"test_name": "volume_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Row Count", "metrics": [{"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-02T04:00:00", "end_time": "1969-12-02T08:00:00", "id": "a614a1bc9cdb72850dd39d211617ccd1", "metric_id": "c7c8b14d8abb09137aa608a9486ea738", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-02T04:00:00", "bucket_end": "1969-12-02T08:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-02T08:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-02T08:00:00", "end_time": "1969-12-02T12:00:00", "id": "94c3acdb1ebd9bc3cec0886c8cf02dc1", "metric_id": "2628ae2f6ca1d4c5d257ea7a88f339cb", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-02T08:00:00", "bucket_end": "1969-12-02T12:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-02T12:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-02T12:00:00", "end_time": "1969-12-02T16:00:00", "id": "0f8962626fccf59936165bd07a033c93", "metric_id": "655558c739a4615d7597e0b3e504524a", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-02T12:00:00", "bucket_end": "1969-12-02T16:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-02T16:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-02T16:00:00", "end_time": "1969-12-02T20:00:00", "id": "d6d4417ff28e9212e6d212d0cebc10a3", "metric_id": "6c3768b980a712dc0e457822bbee9628", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-02T16:00:00", "bucket_end": "1969-12-02T20:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-02T20:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-02T20:00:00", "end_time": "1969-12-03T00:00:00", "id": "765b3f0c139947d7d8a29ddde5a845fa", "metric_id": "762ca0b921a7dadcdaefffd1fa62850c", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-02T20:00:00", "bucket_end": "1969-12-03T00:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-03T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-03T04:00:00", "id": "355340fccdb6672dd5795b7377b325e2", "metric_id": "b134b8375c4fde27a98135c0500f7a62", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-03T04:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-03T04:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-03T04:00:00", "end_time": "1969-12-03T08:00:00", "id": "37708df1be2dad07f481a46185618deb", "metric_id": "89b2e74bc8e92154ef87441f5049fd4c", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T04:00:00", "bucket_end": "1969-12-03T08:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-03T08:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-03T08:00:00", "end_time": "1969-12-03T12:00:00", "id": "cf5bdf404edf5e540991ce1fdd646bce", "metric_id": "a95f346acbbfa2937a892986df183b49", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T08:00:00", "bucket_end": "1969-12-03T12:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-03T12:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-03T12:00:00", "end_time": "1969-12-03T16:00:00", "id": "304894d507ca667c51387250166e61ee", "metric_id": "877947b28687a56e976a1d6f5151425a", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T12:00:00", "bucket_end": "1969-12-03T16:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-03T16:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-03T16:00:00", "end_time": "1969-12-03T20:00:00", "id": "933129fcf77c08437ce18d69cfce85b9", "metric_id": "be3d7e324a8ee90a2c6b1e3f0f070aff", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T16:00:00", "bucket_end": "1969-12-03T20:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-03T20:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-03T20:00:00", "end_time": "1969-12-04T00:00:00", "id": "951e39b4d057d0d4a00fa523c3538f34", "metric_id": "dfc040080a40cbb739efb0274e9b4912", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T20:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-04T04:00:00", "id": "5b2d8e023188ae76067426bfa7bc49fc", "metric_id": "fdb2a40a2187ff5f4aeb3c4556f59a27", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-04T04:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-04T04:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-04T04:00:00", "end_time": "1969-12-04T08:00:00", "id": "f220b8413365a24d2ef39f57fbc72333", "metric_id": "45ccf249d6db4d4daee282de4d569b67", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T04:00:00", "bucket_end": "1969-12-04T08:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-04T08:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-04T08:00:00", "end_time": "1969-12-04T12:00:00", "id": "f3fad1c3968b97547fefed94d8a856d5", "metric_id": "a3e156a883c99cc789ac76ae3a1fd28b", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T08:00:00", "bucket_end": "1969-12-04T12:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-04T12:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-04T12:00:00", "end_time": "1969-12-04T16:00:00", "id": "200449526c69471ef1b1e740d300183c", "metric_id": "3e0c392202aa2ef80c27e4e98a09169d", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T12:00:00", "bucket_end": "1969-12-04T16:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-04T16:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-04T16:00:00", "end_time": "1969-12-04T20:00:00", "id": "3292bef687bc1372098b94316ccfaf14", "metric_id": "8511ecf4920c7ce5172f76126e18e600", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T16:00:00", "bucket_end": "1969-12-04T20:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-04T20:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-04T20:00:00", "end_time": "1969-12-05T00:00:00", "id": "308ccc5e94ad7c5673bd7ccd04bc8931", "metric_id": "8bb19f7dc3b3815bb61981fd8d36b6c6", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T20:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-05T04:00:00", "id": "65b87c5193522f65fc5d00a3a480240b", "metric_id": "3accd6d43d265504af9a74b7775f9ef7", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-05T04:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-05T04:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-05T04:00:00", "end_time": "1969-12-05T08:00:00", "id": "5dcfd7116f093bab7457c48cc50e1525", "metric_id": "29bf2fde4fa5bf5121666a307f3fbb9f", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T04:00:00", "bucket_end": "1969-12-05T08:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-05T08:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-05T08:00:00", "end_time": "1969-12-05T12:00:00", "id": "1751dba9a42c9262c0ef00d28b36b1ab", "metric_id": "02079583dd81464eaaaff28325b95fd0", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T08:00:00", "bucket_end": "1969-12-05T12:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-05T12:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-05T12:00:00", "end_time": "1969-12-05T16:00:00", "id": "b9b08ea37e873a56a8f9bab57a92705e", "metric_id": "1339330e9d79740803748d00638e25e6", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T12:00:00", "bucket_end": "1969-12-05T16:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-05T16:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-05T16:00:00", "end_time": "1969-12-05T20:00:00", "id": "4b0884394fed7de3907a1cf4e353db31", "metric_id": "9f79f1132013e37698c1ca401667ee2a", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T16:00:00", "bucket_end": "1969-12-05T20:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-05T20:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-05T20:00:00", "end_time": "1969-12-06T00:00:00", "id": "8b1f0e913cb6774446b8396e6c7724eb", "metric_id": "a54d6714db298837e93530bba3dce9a1", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T20:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-06T04:00:00", "id": "18e014109b4a7bc4bb4e201458d0f964", "metric_id": "27334f4c26a425a7cb33ff6deb77f469", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-06T04:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-06T04:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-06T04:00:00", "end_time": "1969-12-06T08:00:00", "id": "49d8beccd3058d08d403b298137df098", "metric_id": "4537ec8c0a932f0ff01f3b4bb7b8d7e6", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T04:00:00", "bucket_end": "1969-12-06T08:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-06T08:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-06T08:00:00", "end_time": "1969-12-06T12:00:00", "id": "ccba3955a430ffbc35253ef590c0a87c", "metric_id": "c1e60b5768489365358bb1729becc00a", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T08:00:00", "bucket_end": "1969-12-06T12:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-06T12:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-06T12:00:00", "end_time": "1969-12-06T16:00:00", "id": "d17355077b836cbe604a15d1fa5a1d8d", "metric_id": "3641a0a903099f8d0f420a690371f462", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T12:00:00", "bucket_end": "1969-12-06T16:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-06T16:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-06T16:00:00", "end_time": "1969-12-06T20:00:00", "id": "406314db2e620abe3b24c535b03eada9", "metric_id": "855848203f8c3a15a62641d853eb7ff3", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T16:00:00", "bucket_end": "1969-12-06T20:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-06T20:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-06T20:00:00", "end_time": "1969-12-07T00:00:00", "id": "aff9b562df68b476332fe481192c7b24", "metric_id": "fec6f61afbfbe6aa7c1a6077fb469dce", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T20:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 30.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-07T04:00:00", "id": "605772d5c47a618613f51ca0a6ab184c", "metric_id": "8dc0d219e69c25e6dcbe0b04a7361b68", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-07T04:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 31.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-07T04:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-07T04:00:00", "end_time": "1969-12-07T08:00:00", "id": "d4e0d795768ca91ff1d5cb3c510c52cf", "metric_id": "d2d0e56a232b9b2c8ff667f2ef93f5f7", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T04:00:00", "bucket_end": "1969-12-07T08:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 32.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-07T08:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-07T08:00:00", "end_time": "1969-12-07T12:00:00", "id": "58a024b25fdc9088520d84524f8e3adb", "metric_id": "2641f10f0e57366365258c37c20b65f7", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T08:00:00", "bucket_end": "1969-12-07T12:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 33.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-07T12:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-07T12:00:00", "end_time": "1969-12-07T16:00:00", "id": "15c10eec03e787936186ede355815634", "metric_id": "48dd626e841fc9ccce23db63f6b49ee3", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T12:00:00", "bucket_end": "1969-12-07T16:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 34.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-07T16:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-07T16:00:00", "end_time": "1969-12-07T20:00:00", "id": "9068c4d8871f055bbd39398726e79db5", "metric_id": "cb07703afa217cc174124d34498a0381", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T16:00:00", "bucket_end": "1969-12-07T20:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 35.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-07T20:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-07T20:00:00", "end_time": "1969-12-08T00:00:00", "id": "426a860f58843a9db2191667441e8acd", "metric_id": "15d3ea9365e7d704931800a5513ac135", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T20:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 36.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-08T04:00:00", "id": "58ed2ae43ba180cbd19b10edf89e830e", "metric_id": "37cccecdbeb77cc6e03d75f614561be1", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-08T04:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 37.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-08T04:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-08T04:00:00", "end_time": "1969-12-08T08:00:00", "id": "bfce0ccf9a8db2f4dbb5d27e70dda5d6", "metric_id": "910852bea7c19d4373090f5324b14f32", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T04:00:00", "bucket_end": "1969-12-08T08:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 38.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-08T08:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-08T08:00:00", "end_time": "1969-12-08T12:00:00", "id": "df1dfeea9c6f42931738db9387bb415e", "metric_id": "976ddd57e5fb48c9f69117a50f742911", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T08:00:00", "bucket_end": "1969-12-08T12:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 39.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-08T12:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-08T12:00:00", "end_time": "1969-12-08T16:00:00", "id": "a756d042415c0dcbe997e6b0fc8a3d21", "metric_id": "f7c8aa0fe63ad267f40a54b791d78bc8", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T12:00:00", "bucket_end": "1969-12-08T16:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 40.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-08T16:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-08T16:00:00", "end_time": "1969-12-08T20:00:00", "id": "b86f4fc254eac18f777b0037f56cff42", "metric_id": "8c10281ceed53c8ede4e8db0e3d61e3a", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T16:00:00", "bucket_end": "1969-12-08T20:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 41.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-08T20:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-08T20:00:00", "end_time": "1969-12-09T00:00:00", "id": "f03e288d5e2ef62746fd2ad666d8c769", "metric_id": "034753bb94908488609405ba9923b717", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T20:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 42.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-09T04:00:00", "id": "b49e9c395cdf2a96294e285d01e867b9", "metric_id": "2362bb4e1cff9d3947159d84c33c46bc", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-09T04:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 43.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-09T04:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-09T04:00:00", "end_time": "1969-12-09T08:00:00", "id": "e8559001bd0ba26100b9aab73511ad72", "metric_id": "3b1feeef4980e36d6a56a78e5bf2eb62", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T04:00:00", "bucket_end": "1969-12-09T08:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 44.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-09T08:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-09T08:00:00", "end_time": "1969-12-09T12:00:00", "id": "2b79f32b3d084335f9ab7c23749b66a6", "metric_id": "9b184448a31108eca70e0c4d53fbf08c", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T08:00:00", "bucket_end": "1969-12-09T12:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 45.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-09T12:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-09T12:00:00", "end_time": "1969-12-09T16:00:00", "id": "2a97b14d21fb57933d47ac4ece6add56", "metric_id": "9dccdf096b263034ce10942153c6870b", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T12:00:00", "bucket_end": "1969-12-09T16:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 46.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-09T16:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-09T16:00:00", "end_time": "1969-12-09T20:00:00", "id": "74ea8efb480155c09e14ede4354fe60c", "metric_id": "f875b9ee629c8d5b6c83a5be1bd4cc3a", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T16:00:00", "bucket_end": "1969-12-09T20:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 47.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-09T20:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-09T20:00:00", "end_time": "1969-12-10T00:00:00", "id": "2439e77d80c1f8fed9a857cd136a51e3", "metric_id": "3bf5a44369f0ccad56300b6b464e80cb", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T20:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 48.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-10T04:00:00", "id": "b9f6459d9ba8067f164ddd24acb0a0b6", "metric_id": "a76bd86d7c326a2c605f3a88e42716cd", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-10T04:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 49.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-10T04:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-10T04:00:00", "end_time": "1969-12-10T08:00:00", "id": "5bea3ecab106421a5b92f3ad2cff1e85", "metric_id": "0a2caf7fd7401db5c1ddee33a934dbdb", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T04:00:00", "bucket_end": "1969-12-10T08:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 50.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-10T08:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-10T08:00:00", "end_time": "1969-12-10T12:00:00", "id": "86656e0439261ea6b527ca469b4abc52", "metric_id": "352b1a64be32fbdb435097ff701eb9fa", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T08:00:00", "bucket_end": "1969-12-10T12:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 51.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-10T12:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-10T12:00:00", "end_time": "1969-12-10T16:00:00", "id": "1da560c600f2ff64469b9c2cbf37db71", "metric_id": "6723cee0e539608884407d4f67be24ac", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T12:00:00", "bucket_end": "1969-12-10T16:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 52.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-10T16:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-10T16:00:00", "end_time": "1969-12-10T20:00:00", "id": "eb800515df455d7f04a25f03496dd092", "metric_id": "387034b9e8cebd1b2ffdc5193a7ef2a3", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T16:00:00", "bucket_end": "1969-12-10T20:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 53.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-10T20:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-10T20:00:00", "end_time": "1969-12-11T00:00:00", "id": "a947d95ef5a7c90a4bb1abe34d96a059", "metric_id": "085ef1d92ceca92a1b2d98d11f45fa3a", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T20:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 54.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-11T04:00:00", "id": "c11acf0105a74bf077f3021d34b3c6ac", "metric_id": "182ab67478e62c9b689f822379c9513a", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-11T04:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 55.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-11T04:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-11T04:00:00", "end_time": "1969-12-11T08:00:00", "id": "82bfa23581f965e378b6facddb010231", "metric_id": "8349485df40ecee3d1f1d23e44c05ee2", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T04:00:00", "bucket_end": "1969-12-11T08:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 56.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-11T08:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-11T08:00:00", "end_time": "1969-12-11T12:00:00", "id": "6efb7545589d2d7f94d7a6327b19b4ef", "metric_id": "8233de89f066ba4076360536acc0441b", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T08:00:00", "bucket_end": "1969-12-11T12:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 57.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-11T12:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-11T12:00:00", "end_time": "1969-12-11T16:00:00", "id": "d291dd2c9f270a5a28397cb7ece2db9d", "metric_id": "697cf7824a09ed8c56927190660d6124", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T12:00:00", "bucket_end": "1969-12-11T16:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 58.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-11T16:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-11T16:00:00", "end_time": "1969-12-11T20:00:00", "id": "d02a7328f7f87e4d6b4e58c480e11ec2", "metric_id": "fdf0f3c66f1c2a6b253fc6e76aab91aa", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T16:00:00", "bucket_end": "1969-12-11T20:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 59.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-11T20:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-11T20:00:00", "end_time": "1969-12-12T00:00:00", "id": "031793ca1f39575ba56a25c4e92098c8", "metric_id": "95db856e9943eb25505d2e0e6833e352", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T20:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 60.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-12T04:00:00", "id": "c5619f2a7f028c34f7662bab1624266b", "metric_id": "b940cad367ee9c0b769850c8fac1fe47", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-12T04:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 61.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-12T04:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-12T04:00:00", "end_time": "1969-12-12T08:00:00", "id": "318b6d50fb0c1aa14f1256c5b4589e7f", "metric_id": "3f578827811fb7d2fce27cdeb890cb65", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T04:00:00", "bucket_end": "1969-12-12T08:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 62.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-12T08:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-12T08:00:00", "end_time": "1969-12-12T12:00:00", "id": "9e6ae5580ffa4428f152cc22dd8e6473", "metric_id": "5a55380d4cbb20e1c45b38ae9ca63cbb", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T08:00:00", "bucket_end": "1969-12-12T12:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 63.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-12T12:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-12T12:00:00", "end_time": "1969-12-12T16:00:00", "id": "e063f609243c2f816153f9734bf73c15", "metric_id": "25cf8fe4450266e1060a43f0c55a579d", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T12:00:00", "bucket_end": "1969-12-12T16:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 64.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-12T16:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-12T16:00:00", "end_time": "1969-12-12T20:00:00", "id": "0f4d3d5f4b4300865530b2e071e23dc9", "metric_id": "fe652bf5b74c954f87fa8cdb2193f4d5", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T16:00:00", "bucket_end": "1969-12-12T20:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 65.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-12T20:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-12T20:00:00", "end_time": "1969-12-13T00:00:00", "id": "1da56a8aad043762b439deb5363afaa4", "metric_id": "def9f98e70df8f5dfd0b5a00ecef4901", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T20:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 66.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-13T04:00:00", "id": "6f7090c24363d77cce55f6d5f01e7bf5", "metric_id": "51fde9c535955eceb8acb35733bbcdff", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-13T04:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 67.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-13T04:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-13T04:00:00", "end_time": "1969-12-13T08:00:00", "id": "eb8ebaadc178742aa022e4534b6f6819", "metric_id": "eae7af027ffa511b4d4b6a7fce3a551e", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T04:00:00", "bucket_end": "1969-12-13T08:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 68.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-13T08:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-13T08:00:00", "end_time": "1969-12-13T12:00:00", "id": "39161a758faab746c2759694468d4404", "metric_id": "8c9466f64c27dce071b21b1825c3cda3", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T08:00:00", "bucket_end": "1969-12-13T12:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 69.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-13T12:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-13T12:00:00", "end_time": "1969-12-13T16:00:00", "id": "0f20a54d84222242e832aae14aa21e23", "metric_id": "a21fee294780942ee7a04f581b081657", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T12:00:00", "bucket_end": "1969-12-13T16:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 70.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-13T16:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-13T16:00:00", "end_time": "1969-12-13T20:00:00", "id": "ac3bf0ea2d097eaec8f3c893f126e237", "metric_id": "f66ba9c7042ed6a322efc10b7597a8c0", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T16:00:00", "bucket_end": "1969-12-13T20:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 71.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-13T20:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-13T20:00:00", "end_time": "1969-12-14T00:00:00", "id": "0a70cb80dc7f7539cefd3b9b33868b5c", "metric_id": "9102e9230d834e6ee13c46725e3f5b6e", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T20:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 72.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-14T04:00:00", "id": "0f3b65b5ec3a88a6662053a3e001ab7a", "metric_id": "f4472e8be6ca8a96c316bb54a4d3c688", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-14T04:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 73.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-14T04:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-14T04:00:00", "end_time": "1969-12-14T08:00:00", "id": "ac499347daba5338344fdedda1fb7a3a", "metric_id": "2960dac8dbc3a39f9ee9c12dccfcd26e", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T04:00:00", "bucket_end": "1969-12-14T08:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 74.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-14T08:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-14T08:00:00", "end_time": "1969-12-14T12:00:00", "id": "c2557ea0b8b0c8301690934ddcf04331", "metric_id": "e67f6789e8ec1cf64085508ffeaa20fc", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T08:00:00", "bucket_end": "1969-12-14T12:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 75.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-14T12:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-14T12:00:00", "end_time": "1969-12-14T16:00:00", "id": "1b99aa0522a456a31deb7be1a1cc7f5a", "metric_id": "dea15813fba3c1a18c82ed224ab07fb8", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T12:00:00", "bucket_end": "1969-12-14T16:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 76.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-14T16:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-14T16:00:00", "end_time": "1969-12-14T20:00:00", "id": "a22c7001fc814e27a4e762858787ee27", "metric_id": "1ea8c99dd3b7a24eb76a3bed84e72d04", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T16:00:00", "bucket_end": "1969-12-14T20:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 77.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-14T20:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-14T20:00:00", "end_time": "1969-12-15T00:00:00", "id": "7a7c760eefee55209a8d9ad257f260d5", "metric_id": "09890592d799281a65f28b4ae6c53721", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T20:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 78.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-15T04:00:00", "id": "e972b263f834f3e4571eb08e0d85fa3d", "metric_id": "95918876fc8626f74b265f84c56013f1", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-15T04:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 79.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-15T04:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-15T04:00:00", "end_time": "1969-12-15T08:00:00", "id": "518a576543e7e205ef433286b7c42cf3", "metric_id": "1bc05380491952c460b2655e50fe1ec5", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T04:00:00", "bucket_end": "1969-12-15T08:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 80.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-15T08:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-15T08:00:00", "end_time": "1969-12-15T12:00:00", "id": "2b9e8cc2eb7666735c3bc4425b60c05e", "metric_id": "5897175d7875306145b2f91b0973bba7", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T08:00:00", "bucket_end": "1969-12-15T12:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 81.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-15T12:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-15T12:00:00", "end_time": "1969-12-15T16:00:00", "id": "d25bca7cf89765b49ea08fa9702c4007", "metric_id": "0c6574233f0ca3dc171f37477cda7c06", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T12:00:00", "bucket_end": "1969-12-15T16:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 82.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-15T16:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-15T16:00:00", "end_time": "1969-12-15T20:00:00", "id": "2ab9a718d4d658dc4609aed5cc6cea4f", "metric_id": "71434350a9276c1f269a34059f759d13", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T16:00:00", "bucket_end": "1969-12-15T20:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 83.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-15T20:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-15T20:00:00", "end_time": "1969-12-16T00:00:00", "id": "5525e957c1de9ba814bb23be5c752c0b", "metric_id": "37c31289a62dfe1e516b10b6dff79154", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T20:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 84.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-16T04:00:00", "id": "df027d1bd4bcf562f920844712f44266", "metric_id": "c8b8b3bab980a9d27ae9821e4248e0e0", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-16T04:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 85.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-16T04:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-16T04:00:00", "end_time": "1969-12-16T08:00:00", "id": "771ab53f26ee8974d0a6a4ee47bf10c9", "metric_id": "0f36e0765e0069e536c624d171dafdca", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T04:00:00", "bucket_end": "1969-12-16T08:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 86.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-16T08:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-16T08:00:00", "end_time": "1969-12-16T12:00:00", "id": "944e8f8b75bf06062e76701e87d8aed5", "metric_id": "f97ff39954db757e57e867c21c74048f", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T08:00:00", "bucket_end": "1969-12-16T12:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 87.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-16T12:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-16T12:00:00", "end_time": "1969-12-16T16:00:00", "id": "6510f544e4d13f82b857685c5489c9c4", "metric_id": "314d0917bc5f77e777cb5c3226b3b97b", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T12:00:00", "bucket_end": "1969-12-16T16:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 88.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-16T16:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-16T16:00:00", "end_time": "1969-12-16T20:00:00", "id": "4fd25756bfa75814073ba0d285fd3822", "metric_id": "ae4d04622f35a46a189abbd90b754ce5", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T16:00:00", "bucket_end": "1969-12-16T20:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 89.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-16T20:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-16T20:00:00", "end_time": "1969-12-17T00:00:00", "id": "639ec0428774a4c1e4a926550954b485", "metric_id": "b3aa0afddcad5b69d1d8a7801e903823", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T20:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 90.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-17T04:00:00", "id": "cbd3c1068cc76698238da34fa9c1af01", "metric_id": "b64d6305c68525527a88f3e918dc9a17", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-17T04:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 91.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-17T04:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-17T04:00:00", "end_time": "1969-12-17T08:00:00", "id": "f2836d08c3b761c91d74149eb41a7bc7", "metric_id": "588565337bb1d1d661482b53c32ec839", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T04:00:00", "bucket_end": "1969-12-17T08:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 92.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-17T08:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-17T08:00:00", "end_time": "1969-12-17T12:00:00", "id": "f6db680af4eed4ed802171b1ed3ef5bf", "metric_id": "f4b34f89e234b524ca2064e6df3f7ba4", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T08:00:00", "bucket_end": "1969-12-17T12:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 93.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-17T12:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-17T12:00:00", "end_time": "1969-12-17T16:00:00", "id": "6800c9737d7bb8428fd2ee2b651e5158", "metric_id": "5c5723a8911329b33501eb1392d53d8d", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T12:00:00", "bucket_end": "1969-12-17T16:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 94.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-17T16:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-17T16:00:00", "end_time": "1969-12-17T20:00:00", "id": "6be24bdf185261e5b46b9cdd7ff62056", "metric_id": "aeb31af1fdb8fdcdf622f44d749744b7", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T16:00:00", "bucket_end": "1969-12-17T20:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 95.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-17T20:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-17T20:00:00", "end_time": "1969-12-18T00:00:00", "id": "4dad90d65d6335a651e27e325840bb67", "metric_id": "1f091cc5c37ac9813479e94b1559ca81", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T20:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 96.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-18T04:00:00", "id": "5998422a406c1fb61abea036e10f5151", "metric_id": "28c60d156b46f77fb286efe4531b3ec5", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-18T04:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 97.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-18T04:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-18T04:00:00", "end_time": "1969-12-18T08:00:00", "id": "fa12f65ce0630e5eb699af82373aa405", "metric_id": "9f0d31ed70701348abf0e8588144fa33", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T04:00:00", "bucket_end": "1969-12-18T08:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 98.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-18T08:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-18T08:00:00", "end_time": "1969-12-18T12:00:00", "id": "e99d0befed2ed89d679799606112df93", "metric_id": "0e5da55599fc85b11bac478208033372", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T08:00:00", "bucket_end": "1969-12-18T12:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 99.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-18T12:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-18T12:00:00", "end_time": "1969-12-18T16:00:00", "id": "60fc5c80f5cd557b331c518663c73db5", "metric_id": "f93f4ae9c33eca4662d8b1ed404397c7", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T12:00:00", "bucket_end": "1969-12-18T16:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 100.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-18T16:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-18T16:00:00", "end_time": "1969-12-18T20:00:00", "id": "6d8cd51e437c11e9c02e5e10a375f9e1", "metric_id": "096bc5a5708e72fb2b6decf2bc788226", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T16:00:00", "bucket_end": "1969-12-18T20:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 101.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-18T20:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-18T20:00:00", "end_time": "1969-12-19T00:00:00", "id": "861dac1f2ab943fca0f7f8df552effe5", "metric_id": "01e1f070ccf9ceee6bd9fb11d019dd44", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T20:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 102.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-19T04:00:00", "id": "dcb9125d9395212146ccf789318717e8", "metric_id": "08ffbd82bf34e51b94214a0283959af6", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-19T04:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 103.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-19T04:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-19T04:00:00", "end_time": "1969-12-19T08:00:00", "id": "c8d7055af8f963fc8977867311523484", "metric_id": "ef9d13e0d53d69142240ee3f81fa642a", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T04:00:00", "bucket_end": "1969-12-19T08:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 104.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-19T08:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-19T08:00:00", "end_time": "1969-12-19T12:00:00", "id": "493deff603f96e9abbb69245b0b137c5", "metric_id": "80cc74554cec0f61871c24e5440ec9bb", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T08:00:00", "bucket_end": "1969-12-19T12:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 105.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-19T12:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-19T12:00:00", "end_time": "1969-12-19T16:00:00", "id": "6e4d3bb78e5f3ca1c95cbe8e9bd0706a", "metric_id": "42119b6d6b2d8f2d02b74b82fc12c15d", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T12:00:00", "bucket_end": "1969-12-19T16:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 106.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-19T16:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-19T16:00:00", "end_time": "1969-12-19T20:00:00", "id": "7b35a72d4230c7387789794575777979", "metric_id": "d41c05cf65aff5630a980192de025b8b", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T16:00:00", "bucket_end": "1969-12-19T20:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 107.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-19T20:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-19T20:00:00", "end_time": "1969-12-20T00:00:00", "id": "399a0c645369c0d4425254eb53f746f9", "metric_id": "9e137288d287df822ec36ee7b25f9e93", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T20:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 108.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-20T04:00:00", "id": "08e802563d5e02800bef5e0b000f0636", "metric_id": "c215bc6af69caf502a9cdb6a97a899e1", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-20T04:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 109.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-20T04:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-20T04:00:00", "end_time": "1969-12-20T08:00:00", "id": "9c03a06bacd0d442b2bee03563d2801b", "metric_id": "9e0bbcd25b3ab6fbc30f8031d8cca206", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T04:00:00", "bucket_end": "1969-12-20T08:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 110.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-20T08:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-20T08:00:00", "end_time": "1969-12-20T12:00:00", "id": "82589f024040f423e1734f25a3b6fdd9", "metric_id": "cd547ad20127608d3ef132b18a0d92c0", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T08:00:00", "bucket_end": "1969-12-20T12:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 111.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-20T12:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-20T12:00:00", "end_time": "1969-12-20T16:00:00", "id": "cdf3e2a59fe0ab6e0cd66d62d52ae801", "metric_id": "e64326ae3f4b6956b8974ad2b9233627", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T12:00:00", "bucket_end": "1969-12-20T16:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 112.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-20T16:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-20T16:00:00", "end_time": "1969-12-20T20:00:00", "id": "470eb2ea67c56f5d8f87916628a20cc5", "metric_id": "c797a95cf8de908b04e9cdb4f33371ab", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T16:00:00", "bucket_end": "1969-12-20T20:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 113.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-20T20:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-20T20:00:00", "end_time": "1969-12-21T00:00:00", "id": "3542b43f5a55a27b44ae815dbf7c991d", "metric_id": "507ab69a20165b6a44bfcfa6b6533b2d", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T20:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 114.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-21T04:00:00", "id": "b88f45ebeeb8259b536718eb5e519636", "metric_id": "0f52965963ea2209a2476bb50fa79a96", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-21T04:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 115.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-21T04:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-21T04:00:00", "end_time": "1969-12-21T08:00:00", "id": "f4e2a58db765b00029ac607b5fa99977", "metric_id": "2adb9b4bdf54d4d401c76f92397b6768", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T04:00:00", "bucket_end": "1969-12-21T08:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 116.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-21T08:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-21T08:00:00", "end_time": "1969-12-21T12:00:00", "id": "715f65bb45e8fa6b01406cb853d519a9", "metric_id": "789b890b4e610505f31fc378fd738c71", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T08:00:00", "bucket_end": "1969-12-21T12:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 117.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-21T12:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-21T12:00:00", "end_time": "1969-12-21T16:00:00", "id": "2952ff8cf788ec7ff1bf97a5c33f0c07", "metric_id": "7fc5c70851c08a23a6a67456732168ff", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T12:00:00", "bucket_end": "1969-12-21T16:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 118.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-21T16:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-21T16:00:00", "end_time": "1969-12-21T20:00:00", "id": "dafb5e3b5fdfea92aac5c42303a7cb10", "metric_id": "bba94d4f206a1003d9609df4eaeb76c6", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T16:00:00", "bucket_end": "1969-12-21T20:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 119.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-21T20:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-21T20:00:00", "end_time": "1969-12-22T00:00:00", "id": "40a0fae077733bd94c14b6ab2a54c8c2", "metric_id": "3f4d246ed70e42777e54348bb76d1d19", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T20:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 120.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-22T04:00:00", "id": "38f7e7de337534f874949260e3c8cc28", "metric_id": "3f5f69c4230ee607d5dc62948818df29", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-22T04:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 121.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-22T04:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-22T04:00:00", "end_time": "1969-12-22T08:00:00", "id": "3ba1e2611f5ecafbe2c8ab9b433177a3", "metric_id": "05b4e266b4f1e3d606a0293c7ab3e3f1", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T04:00:00", "bucket_end": "1969-12-22T08:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 122.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-22T08:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-22T08:00:00", "end_time": "1969-12-22T12:00:00", "id": "2795a58857cf5c5964f9b6d2b5a80eb7", "metric_id": "b832f2b45980bfc3f1f466d8659ab413", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T08:00:00", "bucket_end": "1969-12-22T12:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 123.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-22T12:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-22T12:00:00", "end_time": "1969-12-22T16:00:00", "id": "df9b4fe8898274fc15808eabca1cc263", "metric_id": "313ecffac3ab20840b5cf9a8a01ae924", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T12:00:00", "bucket_end": "1969-12-22T16:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 124.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-22T16:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-22T16:00:00", "end_time": "1969-12-22T20:00:00", "id": "837ab24db12dc657da21a22612f4abca", "metric_id": "20d6f4cf4dcee496b2fd3138a4186826", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T16:00:00", "bucket_end": "1969-12-22T20:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 125.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-22T20:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-22T20:00:00", "end_time": "1969-12-23T00:00:00", "id": "af8adae68f588ea04fb491a2c118c696", "metric_id": "ffa793b645c18dbe8ef215abaaa57a4e", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T20:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 126.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-23T04:00:00", "id": "672ed7d15a0bcbeeb09ecdb350fa5459", "metric_id": "3a61072b40529608561911a204be97d4", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-23T04:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 127.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-23T04:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-23T04:00:00", "end_time": "1969-12-23T08:00:00", "id": "e65a5c5bd3d4efcd844a57d9ea102622", "metric_id": "bf0aebd9b470a69e1c96c72ffcbbc698", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T04:00:00", "bucket_end": "1969-12-23T08:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 128.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-23T08:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-23T08:00:00", "end_time": "1969-12-23T12:00:00", "id": "075c5042b066172ae06ee05847bb26f8", "metric_id": "c26d51590469e5962e1d419540015ffe", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T08:00:00", "bucket_end": "1969-12-23T12:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 129.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-23T12:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-23T12:00:00", "end_time": "1969-12-23T16:00:00", "id": "91e3ac9a9eb2dafe89f20e7dce87b18f", "metric_id": "d43c8c4be498243d08b75b00f3366afe", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T12:00:00", "bucket_end": "1969-12-23T16:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 130.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-23T16:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-23T16:00:00", "end_time": "1969-12-23T20:00:00", "id": "0f7e6accb99e0a993c4a756da3d5cd90", "metric_id": "564e2aea336ff5d9cfcf61908158804c", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T16:00:00", "bucket_end": "1969-12-23T20:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 131.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-23T20:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-23T20:00:00", "end_time": "1969-12-24T00:00:00", "id": "bc1e7106102a35170da9eeaaae0ee841", "metric_id": "f6222d4f95dd268f233f1dbecf646fb9", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T20:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 132.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-24T04:00:00", "id": "a537c05880b59076928064ee8ad9b017", "metric_id": "aa73860318177fbd8e958a406b1e968a", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-24T04:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 133.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-24T04:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-24T04:00:00", "end_time": "1969-12-24T08:00:00", "id": "ddfc814826c2095337c59ab29f8a25d1", "metric_id": "ce28d539d77a4d2f6f615a803f88a8e9", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T04:00:00", "bucket_end": "1969-12-24T08:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 134.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-24T08:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-24T08:00:00", "end_time": "1969-12-24T12:00:00", "id": "afa5182a368b6c261289eae40ca89eb3", "metric_id": "3bd9586a0e320397e9202efd409ea931", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T08:00:00", "bucket_end": "1969-12-24T12:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 135.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-24T12:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-24T12:00:00", "end_time": "1969-12-24T16:00:00", "id": "4f24f0827273fe95f469bddf2d8e3896", "metric_id": "f4e24e4e1881b9844c3b8ebf166ba842", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T12:00:00", "bucket_end": "1969-12-24T16:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 136.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-24T16:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-24T16:00:00", "end_time": "1969-12-24T20:00:00", "id": "29693868bec484f8874f613922b9e49b", "metric_id": "a5e3696989f4f29b25b62114925d1b0e", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T16:00:00", "bucket_end": "1969-12-24T20:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 137.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-24T20:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-24T20:00:00", "end_time": "1969-12-25T00:00:00", "id": "5054738f9cfe2b1f8c1dcea6351b9145", "metric_id": "e927f9b2d03bb556c268bb3075ebdc1d", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T20:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 138.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-25T04:00:00", "id": "184be95b51c0a9427140d0c351db1d56", "metric_id": "f035e6877d8ac93063df8a184bef3d42", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-25T04:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 139.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-25T04:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-25T04:00:00", "end_time": "1969-12-25T08:00:00", "id": "3027254d574c075b6f0bc98aba3ba199", "metric_id": "508ec837fb6e4617e804d1cace0ed129", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T04:00:00", "bucket_end": "1969-12-25T08:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 140.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-25T08:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-25T08:00:00", "end_time": "1969-12-25T12:00:00", "id": "63ed6c3a855e33c1216e5f1a514703f2", "metric_id": "1107015f98ac2410c78607e6255a2693", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T08:00:00", "bucket_end": "1969-12-25T12:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 141.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-25T12:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-25T12:00:00", "end_time": "1969-12-25T16:00:00", "id": "67b84427a35ed94a6a3c0c7f24c2c4bd", "metric_id": "26bd8277dde9b8a3bca322f607987d36", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T12:00:00", "bucket_end": "1969-12-25T16:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 142.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-25T16:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-25T16:00:00", "end_time": "1969-12-25T20:00:00", "id": "204adf664ed5f594fd2b5bb7311ca385", "metric_id": "cb727625364577be990017cdf8244567", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T16:00:00", "bucket_end": "1969-12-25T20:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 143.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-25T20:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-25T20:00:00", "end_time": "1969-12-26T00:00:00", "id": "9f1dfe25ee7a2bf4bb2c2dbeacf7a9ee", "metric_id": "5a9a540648844ad519fb125735f1f98a", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T20:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 144.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-26T04:00:00", "id": "4104cf10741f549548a9f8ff41a35eab", "metric_id": "58cd8bc292b7a886434752dbcba1f207", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-26T04:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 145.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-26T04:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-26T04:00:00", "end_time": "1969-12-26T08:00:00", "id": "895a131975dc1eb84b0930df15cafaf9", "metric_id": "810d8de6ffd2d99bdb9638e5b2a6a23c", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T04:00:00", "bucket_end": "1969-12-26T08:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 146.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-26T08:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-26T08:00:00", "end_time": "1969-12-26T12:00:00", "id": "cc98424bc70620806d4255f055c6afe2", "metric_id": "1ad509518f77e117bda3ba8e4c1b0a74", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T08:00:00", "bucket_end": "1969-12-26T12:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 147.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-26T12:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-26T12:00:00", "end_time": "1969-12-26T16:00:00", "id": "34867ff24e5fa43ef0936ba13cca58bf", "metric_id": "d0fce211a23d7a09bf563a94dba4e507", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T12:00:00", "bucket_end": "1969-12-26T16:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 148.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-26T16:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-26T16:00:00", "end_time": "1969-12-26T20:00:00", "id": "090ad8f9374ec86d093801a5e2493de6", "metric_id": "f925e1328c9fb1424f2e93f3b7bf774b", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T16:00:00", "bucket_end": "1969-12-26T20:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 149.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-26T20:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-26T20:00:00", "end_time": "1969-12-27T00:00:00", "id": "2e3b08a9f1dc4cc89b6c776bd4014c8f", "metric_id": "83c25b93675b19649421536f42434bac", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T20:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 150.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-27T04:00:00", "id": "716ad57a716cac0f183e38b035473df5", "metric_id": "5c13fa944dbb2cb48cad92eb22c5ee25", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-27T04:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 151.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-27T04:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-27T04:00:00", "end_time": "1969-12-27T08:00:00", "id": "ea59fd23504ed691c38b54d492459a47", "metric_id": "0ba7764f084fbd21549861308c2d15db", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T04:00:00", "bucket_end": "1969-12-27T08:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 152.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-27T08:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-27T08:00:00", "end_time": "1969-12-27T12:00:00", "id": "7db13de4e0cb2bb2b0a6520368d6ee8a", "metric_id": "3b2bcd1bd291ef1ccab4c57078629ac7", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T08:00:00", "bucket_end": "1969-12-27T12:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 153.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-27T12:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-27T12:00:00", "end_time": "1969-12-27T16:00:00", "id": "561c49bb5a02c9a06fbd30e23c30eba2", "metric_id": "e38296cda1bf1e237b714644dc90e366", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T12:00:00", "bucket_end": "1969-12-27T16:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 154.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-27T16:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-27T16:00:00", "end_time": "1969-12-27T20:00:00", "id": "698cf44116c1c7ce5098498fc58e5359", "metric_id": "7b8cc0dc1ba26963dcb2b8ad65fef2d4", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T16:00:00", "bucket_end": "1969-12-27T20:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 155.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-27T20:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-27T20:00:00", "end_time": "1969-12-28T00:00:00", "id": "bd10f4ca5f790e5a165aa0a613e856e2", "metric_id": "de841ad93a560693dd17e7ff5ec54d9a", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T20:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 156.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-28T04:00:00", "id": "b0b6fc70c64ad1f9c6b2245fa0965391", "metric_id": "9711c65a56e3adced99a16f3f6c20958", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-28T04:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 157.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-28T04:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-28T04:00:00", "end_time": "1969-12-28T08:00:00", "id": "cd0c7c7ba5c5a29d108029e2008b7807", "metric_id": "0e7dd0ccc0428e8ba5c2ab7a5fd2a69c", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T04:00:00", "bucket_end": "1969-12-28T08:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 158.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-28T08:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-28T08:00:00", "end_time": "1969-12-28T12:00:00", "id": "aae9a98a77ab2c5941efc00d4125d5b3", "metric_id": "fed7cf64f8784d0f7c8b47959eefea0d", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T08:00:00", "bucket_end": "1969-12-28T12:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 159.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-28T12:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-28T12:00:00", "end_time": "1969-12-28T16:00:00", "id": "cbae2d6eebfeb87f4af42f1c34044753", "metric_id": "388c6d9ece1b7b156f992c689546b007", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T12:00:00", "bucket_end": "1969-12-28T16:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 160.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-28T16:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-28T16:00:00", "end_time": "1969-12-28T20:00:00", "id": "525918ef33506505ea2a8d3b47425aa3", "metric_id": "644374177b6c035bb82ceb98dbe849c1", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T16:00:00", "bucket_end": "1969-12-28T20:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 161.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-28T20:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-28T20:00:00", "end_time": "1969-12-29T00:00:00", "id": "0c1b6bfdaad45f1984dc39b35f6ea264", "metric_id": "a3009e11376af595fb6ece794137157e", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T20:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 162.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-29T04:00:00", "id": "a6608825d9b5ca647ebdd4b0ad4f552c", "metric_id": "2096ab5fa1f8a8fea8f3c193886eb9c0", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-29T04:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 163.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-29T04:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-29T04:00:00", "end_time": "1969-12-29T08:00:00", "id": "fe995b21b373b0886e22eead483ba8ec", "metric_id": "13eee33ca04a735b3fc0a3e2972fe30e", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T04:00:00", "bucket_end": "1969-12-29T08:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 164.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-29T08:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-29T08:00:00", "end_time": "1969-12-29T12:00:00", "id": "75da462628e70f1a5dda4af90c9cca59", "metric_id": "320e20f8e6f04242210595bbf42f32d2", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T08:00:00", "bucket_end": "1969-12-29T12:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 165.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-29T12:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-29T12:00:00", "end_time": "1969-12-29T16:00:00", "id": "aed76eb75b8f606a517a046f1ee4c9df", "metric_id": "f10d31f4f1867ac058093d1d3d4e9552", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T12:00:00", "bucket_end": "1969-12-29T16:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 166.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-29T16:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-29T16:00:00", "end_time": "1969-12-29T20:00:00", "id": "1b35f7bec954c303ac712f647731d879", "metric_id": "bfa16645d1e2dc41d17d745e0e15b05a", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T16:00:00", "bucket_end": "1969-12-29T20:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 167.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-29T20:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-29T20:00:00", "end_time": "1969-12-30T00:00:00", "id": "fac781e876a388251480038f6f7ee92f", "metric_id": "e1e7da22c1f3bf7ed0c9d750615970a1", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T20:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 168.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 300.0, "average": 300.0, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-30T04:00:00", "id": "e322b72a74ca81007d4ef06e81078510", "metric_id": "200979fadd4924ccb8387337ef0da1bb", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-30T04:00:00", "metric_value": 300.0, "min_metric_value": 300.0, "max_metric_value": 300.0, "training_avg": 300.0, "training_stddev": 0.0, "training_set_size": 169.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-30T04:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 300. The average for this metric is 300.", "is_anomalous": false}, {"value": 0.0, "average": 298.235294118, "min_value": 300.0, "max_value": 300.0, "start_time": "1969-12-30T04:00:00", "end_time": "1969-12-30T08:00:00", "id": "82d66643075b9ca7649bdf2ee2f433d4", "metric_id": "78bde2d29cf21d2793a6fc23c3137593", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": -12.961708312, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T04:00:00", "bucket_end": "1969-12-30T08:00:00", "metric_value": 0.0, "min_metric_value": 229.208445121, "max_metric_value": 367.262143114, "training_avg": 298.235294118, "training_stddev": 23.008949665, "training_set_size": 170.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-30T08:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 0. The average for this metric is 298.235.", "is_anomalous": true}, {"value": 0.0, "average": 296.49122807, "min_value": 229.208445121, "max_value": 367.262143114, "start_time": "1969-12-30T08:00:00", "end_time": "1969-12-30T12:00:00", "id": "ce7e02290ef7057d45813d2d3fbccb31", "metric_id": "39a031bbeb958da301f4010c7fedb8ad", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": -9.165470416, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T08:00:00", "bucket_end": "1969-12-30T12:00:00", "metric_value": 0.0, "min_metric_value": 199.44507072, "max_metric_value": 393.53738542, "training_avg": 296.49122807, "training_stddev": 32.348719117, "training_set_size": 171.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-30T12:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 0. The average for this metric is 296.491.", "is_anomalous": true}, {"value": 0.0, "average": 294.76744186, "min_value": 199.44507072, "max_value": 393.53738542, "start_time": "1969-12-30T12:00:00", "end_time": "1969-12-30T16:00:00", "id": "5ee3f492792f32a73e25271ba59e07b5", "metric_id": "2836a43bbf80c9a065c90d7b69755ac0", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": -7.483703225, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T12:00:00", "bucket_end": "1969-12-30T16:00:00", "metric_value": 0.0, "min_metric_value": 176.603706736, "max_metric_value": 412.931176985, "training_avg": 294.76744186, "training_stddev": 39.387911708, "training_set_size": 172.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-30T16:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 0. The average for this metric is 294.767.", "is_anomalous": true}, {"value": 0.0, "average": 293.063583815, "min_value": 176.603706736, "max_value": 412.931176985, "start_time": "1969-12-30T16:00:00", "end_time": "1969-12-30T20:00:00", "id": "0621306b5951d78dc796c9d45b4c4fe4", "metric_id": "a0563d7c7acf5507b10fff514f55bd85", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": -6.481186647, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T16:00:00", "bucket_end": "1969-12-30T20:00:00", "metric_value": 0.0, "min_metric_value": 157.410840051, "max_metric_value": 428.716327579, "training_avg": 293.063583815, "training_stddev": 45.217581255, "training_set_size": 173.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-30T20:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 0. The average for this metric is 293.064.", "is_anomalous": true}, {"value": 0.0, "average": 291.379310345, "min_value": 157.410840051, "max_value": 428.716327579, "start_time": "1969-12-30T20:00:00", "end_time": "1969-12-31T00:00:00", "id": "94fa306b82822aceea9e69527741dcd9", "metric_id": "6a368beaadb859531043d328da98eb71", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": -5.797046414, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T20:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 0.0, "min_metric_value": 140.589085702, "max_metric_value": 442.169534987, "training_avg": 291.379310345, "training_stddev": 50.263408214, "training_set_size": 174.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 0. The average for this metric is 291.379.", "is_anomalous": true}, {"value": 0.0, "average": 289.714285714, "min_value": 140.589085702, "max_value": 442.169534987, "start_time": "1969-12-31T00:00:00", "end_time": "1969-12-31T04:00:00", "id": "721b0fc1519b83897a93a94efc4a9a4f", "metric_id": "03b89b934450c6ee95eb1a79e8f13e5e", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": -5.292042544, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1969-12-31T04:00:00", "metric_value": 0.0, "min_metric_value": 125.47848263, "max_metric_value": 453.950088799, "training_avg": 289.714285714, "training_stddev": 54.745267695, "training_set_size": 175.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-31T04:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 0. The average for this metric is 289.714.", "is_anomalous": true}, {"value": 0.0, "average": 288.068181818, "min_value": 125.47848263, "max_value": 453.950088799, "start_time": "1969-12-31T04:00:00", "end_time": "1969-12-31T08:00:00", "id": "9c7974891daa30330d55bdb0b73837db", "metric_id": "b9144b0646f7e736354c82f5841be090", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": -4.899559349, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T04:00:00", "bucket_end": "1969-12-31T08:00:00", "metric_value": 0.0, "min_metric_value": 111.68404524, "max_metric_value": 464.452318396, "training_avg": 288.068181818, "training_stddev": 58.794712193, "training_set_size": 176.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-31T08:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 0. The average for this metric is 288.068.", "is_anomalous": true}, {"value": 0.0, "average": 286.440677966, "min_value": 111.68404524, "max_value": 464.452318396, "start_time": "1969-12-31T08:00:00", "end_time": "1969-12-31T12:00:00", "id": "fda9150af5c32bd2b3e70bf9e5e7f40e", "metric_id": "e6abf76a97e4a50201258500d55b0fd0", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": -4.583192088, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T08:00:00", "bucket_end": "1969-12-31T12:00:00", "metric_value": 0.0, "min_metric_value": 98.946456175, "max_metric_value": 473.934899757, "training_avg": 286.440677966, "training_stddev": 62.49807393, "training_set_size": 177.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-31T12:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 0. The average for this metric is 286.441.", "is_anomalous": true}, {"value": 0.0, "average": 284.831460674, "min_value": 98.946456175, "max_value": 473.934899757, "start_time": "1969-12-31T12:00:00", "end_time": "1969-12-31T16:00:00", "id": "a35c5a3cc50732f2ba7701a71877cde2", "metric_id": "e8ebd3f32011ac4c8e36de7a2fc181f8", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": -4.321143905, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T12:00:00", "bucket_end": "1969-12-31T16:00:00", "metric_value": 0.0, "min_metric_value": 87.084197244, "max_metric_value": 482.578724105, "training_avg": 284.831460674, "training_stddev": 65.915754477, "training_set_size": 178.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-31T16:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 0. The average for this metric is 284.831.", "is_anomalous": true}, {"value": 0.0, "average": 283.240223464, "min_value": 87.084197244, "max_value": 482.578724105, "start_time": "1969-12-31T16:00:00", "end_time": "1969-12-31T20:00:00", "id": "83e55b7714f59ee255aeaa374b8a2b69", "metric_id": "dd1af509360d2d61d1e941d53de3c4a8", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": -4.099461744, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T16:00:00", "bucket_end": "1969-12-31T20:00:00", "metric_value": 0.0, "min_metric_value": 75.964067858, "max_metric_value": 490.516379069, "training_avg": 283.240223464, "training_stddev": 69.092051869, "training_set_size": 179.0, "training_start": "1969-12-02T04:00:00", "training_end": "1969-12-31T20:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 0. The average for this metric is 283.24.", "is_anomalous": true}, {"value": 0.0, "average": 281.666666667, "min_value": 75.964067858, "max_value": 490.516379069, "start_time": "1969-12-31T20:00:00", "end_time": "1970-01-01T00:00:00", "id": "88824671403b0d08d5489aa469ef6431", "metric_id": "53d78bd1eadb191e44ca3d8655fa4230", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "detected_at": "2023-01-02T10:42:19.793000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": null, "metric_name": "row_count", "anomaly_score": -3.908744406, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T20:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 0.0, "min_metric_value": 65.484713508, "max_metric_value": 497.848619825, "training_avg": 281.666666667, "training_stddev": 72.060651053, "training_set_size": 180.0, "training_start": "1969-12-02T04:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 0. The average for this metric is 281.667.", "is_anomalous": true}], "result_description": "The last row_count value is 0. The average for this metric is 281.667."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_INT, the last null_count value is 240. The average for this metric is 58.7.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Null Count", "metrics": [{"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "353743783828c6c215bf765ecef14671", "metric_id": "e49ced23be58cee55bf15dee92a8b5e0", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "e89e89a0bdb92e142c7ada7f3eda190b", "metric_id": "7c16f2ebb0ae483c4f057272343dc5f2", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "3e61d0e3a69705e2f66466d1343a4965", "metric_id": "a5c3e6d80028423de2ddea9daa4c88ef", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "4491b3bed9209fc9266505859a664f8a", "metric_id": "1d56df5d648c7ac4aefe2c7b68599c3d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "6a632142b89c81680f5ea536b5324478", "metric_id": "15d9a5fcef97b52fe1b2099405edf03a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "ecc5e0cdb9aee48b054b482f636e82ee", "metric_id": "0a1c633b5b46bcb7295dcc0d321fca50", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "e382d37261af214bb825a044ff0ed679", "metric_id": "b7eff74dab5a456302577225f796eeae", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "568d8c2a1551d0cefb8f24cd2dedc612", "metric_id": "5203945f81f2f5084fa76d6960ac7378", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "354bf0dba21a15b282594523fa356ab5", "metric_id": "fe66a1f7b8e32169137d367a242bf18e", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "fb783081a4a826b7bf4d612f0e6e714f", "metric_id": "3ac2f16436120ef4bd38fe8fb07286b1", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "29a8b8516f60b20ab9ab55548032c8ba", "metric_id": "51be36b423429aab12090a8c1d8f0f49", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "57dec373d3b905688a655108e424e8a2", "metric_id": "73c354e4338de6e4dcf7bbd8c6927618", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "a619236020dce5f4afae2d6e7dffd732", "metric_id": "31a82dc287500e5637bc58640859c32e", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "270e5b1d654a548e802a56002f9e3e2f", "metric_id": "d96a846d484b42ab59c4f24ca19596e1", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "0bc568f7c2d0e466db3ac6a57b0223bc", "metric_id": "04121e9c996a16f4a17c8c2df350e42c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "ddb6636c3c2f8c687e57c0a1c263035b", "metric_id": "15c1b81af9be9753cb24679c86fde001", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "4ed94c3ee08ed8ecb657d4d9479c4f16", "metric_id": "fbaad752cab97756e1739b8c202fbe34", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "f06675df91f7a77c82b6866e67dd0a55", "metric_id": "94bde73b34160d82e56f058621a5cf8d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "e39667e3eabd011a3836baaafb4ad6c4", "metric_id": "002391cce2cb871ed638162ff80b67ff", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "9e84f51c240771c48bded2ea912e56b4", "metric_id": "6e75dd50fbbdb8b3ec81660b81aa7c2f", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "d75385d433702fa48881d0392b3108c1", "metric_id": "43db2bf14809a89266b3d05da11bf528", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "5debf16eb281242f6dd4bbc26b124a07", "metric_id": "322115175b42b58005dc8a162a9f35da", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "1549343333c987fa499db0cdf331acd9", "metric_id": "2cdbb09400dfbe9347b9891fb4a68ad2", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "6da82cad1ca7c2f08c307ec244e2deff", "metric_id": "756e42d531f48abbffe486caca20492d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "09b54d0731a71a0f7047c73896da4159", "metric_id": "67655628658deb86d9d190bad8d54692", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "e7a743d02b9e7d6307855de234c57726", "metric_id": "0bebbd741cf80a725a0d2615fc7a686f", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "41ca1e470744a978ed90b59ed124f80a", "metric_id": "8555691d596c18360dd1d851bcad592e", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 9.0, "average": 52.448275862, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "ee5ca557eb76c0733f2a5816733c8075", "metric_id": "0ab75dc7849720a502804937fb49a20e", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_count", "anomaly_score": -5.199469469, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 9.0, "min_metric_value": 27.379405208, "max_metric_value": 77.517146516, "training_avg": 52.448275862, "training_stddev": 8.356290218, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_count value is 9. The average for this metric is 52.448.", "is_anomalous": true}, {"value": 240.0, "average": 58.7, "min_value": 27.379405208, "max_value": 77.517146516, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "b33f917729fd5fa393ec239f599c843a", "metric_id": "7f3074becedb5adbfaf1e8f513fc2203", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "null_count", "anomaly_score": 5.148695731, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 240.0, "min_metric_value": -46.938404067, "max_metric_value": 164.338404067, "training_avg": 58.7, "training_stddev": 35.212801356, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last null_count value is 240. The average for this metric is 58.7.", "is_anomalous": true}], "result_description": "In column NULL_COUNT_INT, the last null_count value is 240. The average for this metric is 58.7."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "standard_deviation", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('standard_deviation' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_FLOAT, the last standard_deviation value is 2.34. The average for this metric is 2.227.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('standard_deviation' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Standard Deviation", "metrics": [{"value": 2.25255534, "average": 2.248857187, "min_value": 2.233167255, "max_value": 2.26454712, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "b6e95486d8994170b39b3176c047ffb6", "metric_id": "e915a9327ab1bb653c0df5811a7437ab", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": 0.7071067812, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 2.25255534, "min_metric_value": 2.233167255, "max_metric_value": 2.26454712, "training_avg": 2.248857187, "training_stddev": 0.00522997739, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last standard_deviation value is 2.253. The average for this metric is 2.249.", "is_anomalous": false}, {"value": 2.166777507, "average": 2.221497294, "min_value": 2.078898877, "max_value": 2.364095711, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "558374e4bb0d49fa873d947b90187a08", "metric_id": "661d049ec873830df35816e97435e2b4", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": -1.151200435, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 2.166777507, "min_metric_value": 2.078898877, "max_metric_value": 2.364095711, "training_avg": 2.221497294, "training_stddev": 0.04753280575, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last standard_deviation value is 2.167. The average for this metric is 2.221.", "is_anomalous": false}, {"value": 2.239438184, "average": 2.225982517, "min_value": 2.106481781, "max_value": 2.345483252, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "a2d72ab121d08319db9b8b74698d645f", "metric_id": "83649e651c4089667b9c1147f50c1453", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": 0.3377971142, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 2.239438184, "min_metric_value": 2.106481781, "max_metric_value": 2.345483252, "training_avg": 2.225982517, "training_stddev": 0.03983357841, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last standard_deviation value is 2.239. The average for this metric is 2.226.", "is_anomalous": false}, {"value": 2.248076497, "average": 2.230401313, "min_value": 2.122749195, "max_value": 2.33805343, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "12583c6992a7595fec9db82cfcb9c757", "metric_id": "8e5aa9dc471dd6f82e9fdfea756e079b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": 0.4925639534, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 2.248076497, "min_metric_value": 2.122749195, "max_metric_value": 2.33805343, "training_avg": 2.230401313, "training_stddev": 0.03588403921, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last standard_deviation value is 2.248. The average for this metric is 2.23.", "is_anomalous": false}, {"value": 2.244623289, "average": 2.232771642, "min_value": 2.134921861, "max_value": 2.330621423, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "7363072704ee27cbadee61e581866f2e", "metric_id": "ab57ae79d125af5a52c994bc580c70d8", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": 0.3633624918, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 2.244623289, "min_metric_value": 2.134921861, "max_metric_value": 2.330621423, "training_avg": 2.232771642, "training_stddev": 0.03261659368, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last standard_deviation value is 2.245. The average for this metric is 2.233.", "is_anomalous": false}, {"value": 2.190504609, "average": 2.226733495, "min_value": 2.125364132, "max_value": 2.328102857, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "6aa7f206f50a9b129525a3cf5e725ad3", "metric_id": "c522df2c5f4a29762011b5859fce16c9", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": -1.072184463, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 2.190504609, "min_metric_value": 2.125364132, "max_metric_value": 2.328102857, "training_avg": 2.226733495, "training_stddev": 0.03378978736, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last standard_deviation value is 2.191. The average for this metric is 2.227.", "is_anomalous": false}, {"value": 2.232220318, "average": 2.227419347, "min_value": 2.133389288, "max_value": 2.321449407, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "a68a42a81d651f91679f9ff1d69e22d2", "metric_id": "1ac5bdbb6266784113de38ba7d5d8f56", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": 0.1531734881, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 2.232220318, "min_metric_value": 2.133389288, "max_metric_value": 2.321449407, "training_avg": 2.227419347, "training_stddev": 0.03134335304, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last standard_deviation value is 2.232. The average for this metric is 2.227.", "is_anomalous": false}, {"value": 2.212829095, "average": 2.225798208, "min_value": 2.136639244, "max_value": 2.314957173, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "7aa529bd9969f60874ebfda69ac8eaca", "metric_id": "9fb7d38bcd37fee8d2dc9ea99fea0186", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": -0.4363816955, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 2.212829095, "min_metric_value": 2.136639244, "max_metric_value": 2.314957173, "training_avg": 2.225798208, "training_stddev": 0.0297196549, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last standard_deviation value is 2.213. The average for this metric is 2.226.", "is_anomalous": false}, {"value": 2.213155514, "average": 2.224533939, "min_value": 2.139622709, "max_value": 2.309445169, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "cf2fd8b14f6e2e1e6c45ee3aa22c0aa0", "metric_id": "b090d2d163c12ba5612c1a337a5acb30", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": -0.4020113013, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 2.213155514, "min_metric_value": 2.139622709, "max_metric_value": 2.309445169, "training_avg": 2.224533939, "training_stddev": 0.02830374345, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last standard_deviation value is 2.213. The average for this metric is 2.225.", "is_anomalous": false}, {"value": 2.250518839, "average": 2.226896202, "min_value": 2.142983307, "max_value": 2.310809098, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "f32fcfa8edaa217bb61895499820e68d", "metric_id": "aa9424dfeb976c8ba67f09d6450d06cc", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": 0.8445413341, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 2.250518839, "min_metric_value": 2.142983307, "max_metric_value": 2.310809098, "training_avg": 2.226896202, "training_stddev": 0.02797096508, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last standard_deviation value is 2.251. The average for this metric is 2.227.", "is_anomalous": false}, {"value": 2.242335268, "average": 2.228182791, "min_value": 2.147065453, "max_value": 2.30930013, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "69020aef4fdf0d962f25a083b4fea699", "metric_id": "c4c4113f0ef0d1255bcb6edab4fb390a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": 0.5234075721, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 2.242335268, "min_metric_value": 2.147065453, "max_metric_value": 2.30930013, "training_avg": 2.228182791, "training_stddev": 0.02703911288, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last standard_deviation value is 2.242. The average for this metric is 2.228.", "is_anomalous": false}, {"value": 2.253264711, "average": 2.23011217, "min_value": 2.149693142, "max_value": 2.310531198, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "e2fd7f2bb8fdcab7a9c59b7c762332d1", "metric_id": "ee68b54d9a9f043d12e6f79527c0264d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": 0.8636963892, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 2.253264711, "min_metric_value": 2.149693142, "max_metric_value": 2.310531198, "training_avg": 2.23011217, "training_stddev": 0.02680634263, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last standard_deviation value is 2.253. The average for this metric is 2.23.", "is_anomalous": false}, {"value": 2.228882747, "average": 2.230024354, "min_value": 2.152753963, "max_value": 2.307294744, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "e4ca37e65d5ceadf5d12272e132aecb0", "metric_id": "26865b87220ba510a663398f17a97186", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": -0.04432255653, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 2.228882747, "min_metric_value": 2.152753963, "max_metric_value": 2.307294744, "training_avg": 2.230024354, "training_stddev": 0.02575679684, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last standard_deviation value is 2.229. The average for this metric is 2.23.", "is_anomalous": false}, {"value": 2.184415109, "average": 2.226983737, "min_value": 2.144567988, "max_value": 2.309399486, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "78d269903729af42372c18b526c27a54", "metric_id": "1f43c6d6a5cfc5be1823998a1c5b6c75", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": -1.549532558, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 2.184415109, "min_metric_value": 2.144567988, "max_metric_value": 2.309399486, "training_avg": 2.226983737, "training_stddev": 0.02747191632, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last standard_deviation value is 2.184. The average for this metric is 2.227.", "is_anomalous": false}, {"value": 2.197258183, "average": 2.22512589, "min_value": 2.142442387, "max_value": 2.307809394, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "47344b9f0046738e43d28a9847a93b8b", "metric_id": "0f2af86837d08fe11dacc913479b3570", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": -1.011122137, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 2.197258183, "min_metric_value": 2.142442387, "max_metric_value": 2.307809394, "training_avg": 2.22512589, "training_stddev": 0.0275611679, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last standard_deviation value is 2.197. The average for this metric is 2.225.", "is_anomalous": false}, {"value": 2.282779596, "average": 2.228517285, "min_value": 2.138134682, "max_value": 2.318899887, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "8fe9107f6c88e313c984f9ffc5433611", "metric_id": "d6d88274c4032dbede2c41e5be2a1d8c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": 1.801087045, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 2.282779596, "min_metric_value": 2.138134682, "max_metric_value": 2.318899887, "training_avg": 2.228517285, "training_stddev": 0.0301275341, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last standard_deviation value is 2.283. The average for this metric is 2.229.", "is_anomalous": false}, {"value": 2.253590931, "average": 2.229910265, "min_value": 2.140451736, "max_value": 2.319368794, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "390838ab7ffbd93b69d30134818fb49a", "metric_id": "95b01fd853a90577952b2ad64d101f1c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": 0.7941333039, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 2.253590931, "min_metric_value": 2.140451736, "max_metric_value": 2.319368794, "training_avg": 2.229910265, "training_stddev": 0.02981950961, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last standard_deviation value is 2.254. The average for this metric is 2.23.", "is_anomalous": false}, {"value": 2.173325555, "average": 2.226932122, "min_value": 2.131669943, "max_value": 2.322194302, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "13f280f6a8ce70ab03fcf95dad938af8", "metric_id": "1b18ec499d4ad91f9b93e2f1558a4cc4", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": -1.688179967, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 2.173325555, "min_metric_value": 2.131669943, "max_metric_value": 2.322194302, "training_avg": 2.226932122, "training_stddev": 0.03175405986, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last standard_deviation value is 2.173. The average for this metric is 2.227.", "is_anomalous": false}, {"value": 2.229395087, "average": 2.227055271, "min_value": 2.134319155, "max_value": 2.319791387, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "268b6b82c41f22a9e949f49d8608925f", "metric_id": "2935472387a9e6feefa34f94df8589af", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": 0.07569272437, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 2.229395087, "min_metric_value": 2.134319155, "max_metric_value": 2.319791387, "training_avg": 2.227055271, "training_stddev": 0.0309120387, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last standard_deviation value is 2.229. The average for this metric is 2.227.", "is_anomalous": false}, {"value": 2.220929915, "average": 2.226763587, "min_value": 2.136286696, "max_value": 2.317240478, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "4ba06a256c987f1e96e2e8a28130f6b8", "metric_id": "d71983bd9ae9c4b01b20c91dee24903c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": -0.1934307838, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 2.220929915, "min_metric_value": 2.136286696, "max_metric_value": 2.317240478, "training_avg": 2.226763587, "training_stddev": 0.03015896377, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last standard_deviation value is 2.221. The average for this metric is 2.227.", "is_anomalous": false}, {"value": 2.209731787, "average": 2.225989414, "min_value": 2.13702355, "max_value": 2.314955279, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "4e86f7ef180b2ef007d46521187a3992", "metric_id": "e7c114f23f73b9c3da006f37688561ee", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": -0.5482201769, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 2.209731787, "min_metric_value": 2.13702355, "max_metric_value": 2.314955279, "training_avg": 2.225989414, "training_stddev": 0.02965528827, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last standard_deviation value is 2.21. The average for this metric is 2.226.", "is_anomalous": false}, {"value": 2.21119444, "average": 2.225346155, "min_value": 2.137934434, "max_value": 2.312757875, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "d7a975bcb6710954b2cb09efd68c779b", "metric_id": "ea0074c7b436bb495624bbf4c62cfec9", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": -0.4856916692, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 2.21119444, "min_metric_value": 2.137934434, "max_metric_value": 2.312757875, "training_avg": 2.225346155, "training_stddev": 0.02913724018, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last standard_deviation value is 2.211. The average for this metric is 2.225.", "is_anomalous": false}, {"value": 2.217566031, "average": 2.225021983, "min_value": 2.13939898, "max_value": 2.310644986, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "70d280af09cdef35ccd59fa136939677", "metric_id": "b78e939bc9e0057e4955e301a59003be", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": -0.2612365051, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 2.217566031, "min_metric_value": 2.13939898, "max_metric_value": 2.310644986, "training_avg": 2.225021983, "training_stddev": 0.02854100106, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last standard_deviation value is 2.218. The average for this metric is 2.225.", "is_anomalous": false}, {"value": 2.201202104, "average": 2.224069188, "min_value": 2.139039269, "max_value": 2.309099106, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "2a61ee75fa8ecbc550b2e8599027feed", "metric_id": "846244e5556de688cfe8af05094866f2", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": -0.8067895758, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 2.201202104, "min_metric_value": 2.139039269, "max_metric_value": 2.309099106, "training_avg": 2.224069188, "training_stddev": 0.02834330626, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last standard_deviation value is 2.201. The average for this metric is 2.224.", "is_anomalous": false}, {"value": 2.230453888, "average": 2.224314753, "min_value": 2.140918144, "max_value": 2.307711362, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "3dc0f42f07ea72b093a26daa304491e0", "metric_id": "25c3ab4365de4a68581c5ae68164b625", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": 0.2208411892, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 2.230453888, "min_metric_value": 2.140918144, "max_metric_value": 2.307711362, "training_avg": 2.224314753, "training_stddev": 0.0277988696, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last standard_deviation value is 2.23. The average for this metric is 2.224.", "is_anomalous": false}, {"value": 2.25515211, "average": 2.225456877, "min_value": 2.141764133, "max_value": 2.309149622, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "79fee7dbc48ba1e4d11d1277f219a864", "metric_id": "dcb237c4ae31a4731b89b90848b28a8d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": 1.06443753, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 2.25515211, "min_metric_value": 2.141764133, "max_metric_value": 2.309149622, "training_avg": 2.225456877, "training_stddev": 0.02789758141, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last standard_deviation value is 2.255. The average for this metric is 2.225.", "is_anomalous": false}, {"value": 2.176629288, "average": 2.223713035, "min_value": 2.137044822, "max_value": 2.310381247, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "817465d87cb731dee1c320ac57463a16", "metric_id": "4c1b7923439856aa7fb5d67ffb44b1e1", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": -1.629792947, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 2.176629288, "min_metric_value": 2.137044822, "max_metric_value": 2.310381247, "training_avg": 2.223713035, "training_stddev": 0.02888940415, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last standard_deviation value is 2.177. The average for this metric is 2.224.", "is_anomalous": false}, {"value": 2.202153362, "average": 2.222969598, "min_value": 2.137019788, "max_value": 2.308919408, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "0908a7c455f19122ead68202bc72021c", "metric_id": "b4efd69c9da7cd56934023965d6665b6", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": -0.7265717778, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 2.202153362, "min_metric_value": 2.137019788, "max_metric_value": 2.308919408, "training_avg": 2.222969598, "training_stddev": 0.02864993665, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last standard_deviation value is 2.202. The average for this metric is 2.223.", "is_anomalous": false}, {"value": 2.340126615, "average": 2.226874832, "min_value": 2.137019788, "max_value": 2.308919408, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "11c9f59852a189c8e2631005704388de", "metric_id": "9533cfd801d4d70fd30e1c44f6faf20e", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": 3.203192492, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 2.340126615, "min_metric_value": 2.120807104, "max_metric_value": 2.33294256, "training_avg": 2.226874832, "training_stddev": 0.03535590939, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last standard_deviation value is 2.34. The average for this metric is 2.227.", "is_anomalous": true}], "result_description": "In column NULL_COUNT_FLOAT, the last standard_deviation value is 2.34. The average for this metric is 2.227."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "max", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('max' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_FLOAT, the last max value is 8.9. The average for this metric is 8.891.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('max' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Max", "metrics": [{"value": 8.897574636, "average": 8.896057709, "min_value": 8.889621934, "max_value": 8.902493484, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "eaff3f4836774c2f92afc3c785c60881", "metric_id": "35bfa252d7570a507a6ebd151786a71b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "max", "anomaly_score": 0.7071067788, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 8.897574636, "min_metric_value": 8.889621934, "max_metric_value": 8.902493484, "training_avg": 8.896057709, "training_stddev": 0.00214525824, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last max value is 8.898. The average for this metric is 8.896.", "is_anomalous": false}, {"value": 8.887807122, "average": 8.893307513, "min_value": 8.878309976, "max_value": 8.908305051, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "a1a7051640e0f5b03c8bb84272ee9c6f", "metric_id": "4e4784393611664b51024bb72bdaf582", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "max", "anomaly_score": -1.100258829, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 8.887807122, "min_metric_value": 8.878309976, "max_metric_value": 8.908305051, "training_avg": 8.893307513, "training_stddev": 0.00499917927, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last max value is 8.888. The average for this metric is 8.893.", "is_anomalous": false}, {"value": 8.898612347, "average": 8.894633722, "min_value": 8.880030003, "max_value": 8.90923744, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "c2075bc72e32170dbd94702a0fdf7a41", "metric_id": "559824992d499f002ca60038f8ee8870", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "max", "anomaly_score": 0.8173175577, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 8.898612347, "min_metric_value": 8.880030003, "max_metric_value": 8.90923744, "training_avg": 8.894633722, "training_stddev": 0.00486790616, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last max value is 8.899. The average for this metric is 8.895.", "is_anomalous": false}, {"value": 8.898630261, "average": 8.89543303, "min_value": 8.881696159, "max_value": 8.909169901, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "c3e628599ed9a116107c02d6ebc32168", "metric_id": "12cd4b2c77f13de02cca3cee6d39ad64", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "max", "anomaly_score": 0.6982443916, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 8.898630261, "min_metric_value": 8.881696159, "max_metric_value": 8.909169901, "training_avg": 8.89543303, "training_stddev": 0.004578957007, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last max value is 8.899. The average for this metric is 8.895.", "is_anomalous": false}, {"value": 8.888733033, "average": 8.894316363, "min_value": 8.879541512, "max_value": 8.909091215, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "4ee435679ccab47909b0d8855a2b6d0b", "metric_id": "cb5799cee026b729ee7a5aa997226449", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "max", "anomaly_score": -1.133682623, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 8.888733033, "min_metric_value": 8.879541512, "max_metric_value": 8.909091215, "training_avg": 8.894316363, "training_stddev": 0.004924950538, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last max value is 8.889. The average for this metric is 8.894.", "is_anomalous": false}, {"value": 8.895153792, "average": 8.894435996, "min_value": 8.880915079, "max_value": 8.907956913, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "62aa687e6b45e84b8c22371601512490", "metric_id": "b6f5859c6134875aa240f73a25f572dd", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "max", "anomaly_score": 0.1592634286, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 8.895153792, "min_metric_value": 8.880915079, "max_metric_value": 8.907956913, "training_avg": 8.894435996, "training_stddev": 0.004506972224, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last max value is 8.895. The average for this metric is 8.894.", "is_anomalous": false}, {"value": 8.897392175, "average": 8.894805518, "min_value": 8.881900864, "max_value": 8.907710173, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "8794f22dec6ea0b21598709294e4f173", "metric_id": "2af9e8e72150ea64a818a08731ed71a8", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "max", "anomaly_score": 0.6013311211, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 8.897392175, "min_metric_value": 8.881900864, "max_metric_value": 8.907710173, "training_avg": 8.894805518, "training_stddev": 0.004301551425, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last max value is 8.897. The average for this metric is 8.895.", "is_anomalous": false}, {"value": 8.899851557, "average": 8.895366189, "min_value": 8.882282752, "max_value": 8.908449627, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "aaa59889d924f5a40eb77b6e0fc8d030", "metric_id": "6a7ed8efdf9affb24c28b9797095b7f2", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "max", "anomaly_score": 1.028483741, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 8.899851557, "min_metric_value": 8.882282752, "max_metric_value": 8.908449627, "training_avg": 8.895366189, "training_stddev": 0.004361145941, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last max value is 8.9. The average for this metric is 8.895.", "is_anomalous": false}, {"value": 8.896481423, "average": 8.895477713, "min_value": 8.883097239, "max_value": 8.907858186, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "b59803a3ca1e9a5e2787617f49f90c95", "metric_id": "18e386274b3fd228ebb68a6d7e1c9416", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "max", "anomaly_score": 0.2432160527, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 8.896481423, "min_metric_value": 8.883097239, "max_metric_value": 8.907858186, "training_avg": 8.895477713, "training_stddev": 0.004126824471, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last max value is 8.896. The average for this metric is 8.895.", "is_anomalous": false}, {"value": 8.887913214, "average": 8.894790031, "min_value": 8.881197157, "max_value": 8.908382905, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "f32c04e3f3531ac846c01d8c8eb557ba", "metric_id": "5fcec8ccc13f291cfd0145450c167176", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "max", "anomaly_score": -1.517740291, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 8.887913214, "min_metric_value": 8.881197157, "max_metric_value": 8.908382905, "training_avg": 8.894790031, "training_stddev": 0.004530957986, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last max value is 8.888. The average for this metric is 8.895.", "is_anomalous": false}, {"value": 8.896787538, "average": 8.89495649, "min_value": 8.881881253, "max_value": 8.908031727, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "fa6682f9eae6ec23205d7853d2134159", "metric_id": "6d18b50b99902a720db301057dc875a0", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "max", "anomaly_score": 0.4201182256, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 8.896787538, "min_metric_value": 8.881881253, "max_metric_value": 8.908031727, "training_avg": 8.89495649, "training_stddev": 0.004358412299, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last max value is 8.897. The average for this metric is 8.895.", "is_anomalous": false}, {"value": 8.89893793, "average": 8.895262755, "min_value": 8.882313262, "max_value": 8.908212247, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "ee860b35c4f99af1961b421b795b696a", "metric_id": "0bc69759b42e359aa263042a3d560481", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "max", "anomaly_score": 0.8514252367, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 8.89893793, "min_metric_value": 8.882313262, "max_metric_value": 8.908212247, "training_avg": 8.895262755, "training_stddev": 0.004316497644, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last max value is 8.899. The average for this metric is 8.895.", "is_anomalous": false}, {"value": 8.899906995, "average": 8.895594486, "min_value": 8.882607725, "max_value": 8.908581247, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "59d7e361e4b813992f61383a5a1ebeb8", "metric_id": "c0f924dbb8b8fe389435c284e0120b78", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "max", "anomaly_score": 0.9962089793, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 8.899906995, "min_metric_value": 8.882607725, "max_metric_value": 8.908581247, "training_avg": 8.895594486, "training_stddev": 0.004328920222, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last max value is 8.9. The average for this metric is 8.896.", "is_anomalous": false}, {"value": 8.88949009, "average": 8.895187526, "min_value": 8.881809661, "max_value": 8.908565392, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "0b14411bbcf9188e0c03100486d17406", "metric_id": "f6ece7ff1dbb06497fac18673121f88d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "max", "anomaly_score": -1.277655933, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 8.88949009, "min_metric_value": 8.881809661, "max_metric_value": 8.908565392, "training_avg": 8.895187526, "training_stddev": 0.004459288569, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last max value is 8.889. The average for this metric is 8.895.", "is_anomalous": false}, {"value": 8.898127366, "average": 8.895371266, "min_value": 8.882260292, "max_value": 8.90848224, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "024ce6de01406cf314c1b0028f79bb7e", "metric_id": "d97674f0375c0de06bbe1999e393b6f1", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "max", "anomaly_score": 0.6306396233, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 8.898127366, "min_metric_value": 8.882260292, "max_metric_value": 8.90848224, "training_avg": 8.895371266, "training_stddev": 0.004370324596, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last max value is 8.898. The average for this metric is 8.895.", "is_anomalous": false}, {"value": 8.8968739, "average": 8.895459656, "min_value": 8.882718016, "max_value": 8.908201297, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "c7803e415db5099e0db25f12142468d5", "metric_id": "4f3fa1a5e6eb22a2a179d0f4b93ca324", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "max", "anomaly_score": 0.3329815307, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 8.8968739, "min_metric_value": 8.882718016, "max_metric_value": 8.908201297, "training_avg": 8.895459656, "training_stddev": 0.004247213399, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last max value is 8.897. The average for this metric is 8.895.", "is_anomalous": false}, {"value": 8.888706937, "average": 8.895084505, "min_value": 8.881833127, "max_value": 8.908335884, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "6c5580dfd2d6be345982122659124656", "metric_id": "c592cf3cbed1e05c5974e9dc92703840", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "max", "anomaly_score": -1.443827466, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 8.888706937, "min_metric_value": 8.881833127, "max_metric_value": 8.908335884, "training_avg": 8.895084505, "training_stddev": 0.004417126258, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last max value is 8.889. The average for this metric is 8.895.", "is_anomalous": false}, {"value": 8.89773482, "average": 8.895223996, "min_value": 8.882217429, "max_value": 8.908230562, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "dbf14a9b322dd426b2d9f4f79e55504b", "metric_id": "27653c8db6d6ec38f1257cefda004204", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "max", "anomaly_score": 0.5791285148, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 8.89773482, "min_metric_value": 8.882217429, "max_metric_value": 8.908230562, "training_avg": 8.895223996, "training_stddev": 0.004335522204, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last max value is 8.898. The average for this metric is 8.895.", "is_anomalous": false}, {"value": 8.899907307, "average": 8.895458161, "min_value": 8.882414501, "max_value": 8.908501822, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "9bf018e438da9b10e02b045f2303a106", "metric_id": "30d2c46f146faf26f7d514c9d2a7d0e6", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "max", "anomaly_score": 1.023289247, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 8.899907307, "min_metric_value": 8.882414501, "max_metric_value": 8.908501822, "training_avg": 8.895458161, "training_stddev": 0.00434788688, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last max value is 8.9. The average for this metric is 8.895.", "is_anomalous": false}, {"value": 8.889544538, "average": 8.89517656, "min_value": 8.881886798, "max_value": 8.908466322, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "c8d2e41d37afaf4c02878f667e2d7469", "metric_id": "1e4161eb6b1b59b3ca8a22a686f4d9f8", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "max", "anomaly_score": -1.271359512, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 8.889544538, "min_metric_value": 8.881886798, "max_metric_value": 8.908466322, "training_avg": 8.89517656, "training_stddev": 0.004429920645, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last max value is 8.89. The average for this metric is 8.895.", "is_anomalous": false}, {"value": 8.899382774, "average": 8.895367752, "min_value": 8.882122181, "max_value": 8.908613322, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "97c4a2d12d9e1691d493276ac7bca57e", "metric_id": "18fcc3bfa15379eff105a082a7361590", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "max", "anomaly_score": 0.9093655637, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 8.899382774, "min_metric_value": 8.882122181, "max_metric_value": 8.908613322, "training_avg": 8.895367752, "training_stddev": 0.004415190105, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last max value is 8.899. The average for this metric is 8.895.", "is_anomalous": false}, {"value": 8.893369847, "average": 8.895280886, "min_value": 8.882279644, "max_value": 8.908282128, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "0a122fe39bb0166ffbfcd9dbb4563e22", "metric_id": "a61b8bcb9c78b5a7f0b4b253923d4d12", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "max", "anomaly_score": -0.4409669407, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 8.893369847, "min_metric_value": 8.882279644, "max_metric_value": 8.908282128, "training_avg": 8.895280886, "training_stddev": 0.004333747338, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last max value is 8.893. The average for this metric is 8.895.", "is_anomalous": false}, {"value": 8.896861929, "average": 8.895346763, "min_value": 8.882594491, "max_value": 8.908099035, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "c42290f9a36ec55c44e5292bb91a04dd", "metric_id": "bfd73568a5b51a499198b0c6dfbc8f91", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "max", "anomaly_score": 0.3564460034, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 8.896861929, "min_metric_value": 8.882594491, "max_metric_value": 8.908099035, "training_avg": 8.895346763, "training_stddev": 0.004250757438, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last max value is 8.897. The average for this metric is 8.895.", "is_anomalous": false}, {"value": 8.888358747, "average": 8.895067242, "min_value": 8.881898178, "max_value": 8.908236307, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "971ea622c525aedfa9dddda0e8b83fac", "metric_id": "28d7995762db05cc7a2bb264cd4e8e83", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "max", "anomaly_score": -1.528239718, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 8.888358747, "min_metric_value": 8.881898178, "max_metric_value": 8.908236307, "training_avg": 8.895067242, "training_stddev": 0.004389688055, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last max value is 8.888. The average for this metric is 8.895.", "is_anomalous": false}, {"value": 8.899442967, "average": 8.895235539, "min_value": 8.882078219, "max_value": 8.90839286, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "2b96a5c0b80586b9a76d4b88260de340", "metric_id": "8972a3d46dde0c1369b108f7abd50310", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "max", "anomaly_score": 0.9593352793, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 8.899442967, "min_metric_value": 8.882078219, "max_metric_value": 8.90839286, "training_avg": 8.895235539, "training_stddev": 0.004385773411, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last max value is 8.899. The average for this metric is 8.895.", "is_anomalous": false}, {"value": 8.88962589, "average": 8.895027775, "min_value": 8.881725662, "max_value": 8.908329887, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "995380c654c7ffec7dfef4762c78c261", "metric_id": "317a52818a275cfcba7ddd2f7b10b2e0", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "max", "anomaly_score": -1.218276679, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 8.88962589, "min_metric_value": 8.881725662, "max_metric_value": 8.908329887, "training_avg": 8.895027775, "training_stddev": 0.004434037431, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last max value is 8.89. The average for this metric is 8.895.", "is_anomalous": false}, {"value": 8.894396365, "average": 8.895005224, "min_value": 8.881946864, "max_value": 8.908063585, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "c44d0e524140ab0148f498cddc5cb2e9", "metric_id": "5bfadbbf8b64c9f575ed067088ee4f68", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "max", "anomaly_score": -0.1398779374, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 8.894396365, "min_metric_value": 8.881946864, "max_metric_value": 8.908063585, "training_avg": 8.895005224, "training_stddev": 0.004352786785, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last max value is 8.894. The average for this metric is 8.895.", "is_anomalous": false}, {"value": 8.778370103, "average": 8.890983324, "min_value": 8.881946864, "max_value": 8.908063585, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "2388843c18570d4b53094cfd3fff6af2", "metric_id": "a850caecd679c3597f47c5618ee5d3ae", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "max", "anomaly_score": -5.101081081, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 8.778370103, "min_metric_value": 8.824754292, "max_metric_value": 8.957212355, "training_avg": 8.890983324, "training_stddev": 0.02207634392, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last max value is 8.778. The average for this metric is 8.891.", "is_anomalous": true}, {"value": 8.899528179, "average": 8.891268152, "min_value": 8.826022939, "max_value": 8.956513365, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "f32bbd149814d6d53471a19fb7c91e85", "metric_id": "4992c5c0b6400e5b687fdcb79822916d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "max", "anomaly_score": 0.379799226, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 8.899528179, "min_metric_value": 8.826022939, "max_metric_value": 8.956513365, "training_avg": 8.891268152, "training_stddev": 0.02174840433, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last max value is 8.9. The average for this metric is 8.891.", "is_anomalous": false}], "result_description": "In column NULL_PERCENT_FLOAT, the last max value is 8.9. The average for this metric is 8.891."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "missing_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('missing_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_STR, the last missing_count value is 175. The average for this metric is 346.067.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('missing_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Missing Count", "metrics": [{"value": 345.0, "average": 372.5, "min_value": 255.827381104, "max_value": 489.172618896, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "cf709c256ac5073eec054a30a73ad703", "metric_id": "2cc7d221b5e41f177eba98dc82ff5374", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_count", "anomaly_score": -0.7071067812, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 345.0, "min_metric_value": 255.827381104, "max_metric_value": 489.172618896, "training_avg": 372.5, "training_stddev": 38.890872965, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_count value is 345. The average for this metric is 372.5.", "is_anomalous": false}, {"value": 365.0, "average": 370.0, "min_value": 286.483534558, "max_value": 453.516465442, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "655e015789fdcb7e94c2c1279c8a6bd0", "metric_id": "b9bc7f24f379acb5646d95bcaad861d0", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_count", "anomaly_score": -0.179605302, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 365.0, "min_metric_value": 286.483534558, "max_metric_value": 453.516465442, "training_avg": 370.0, "training_stddev": 27.838821814, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_count value is 365. The average for this metric is 370.", "is_anomalous": false}, {"value": 352.0, "average": 365.5, "min_value": 292.158333807, "max_value": 438.841666193, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "72048f145872f8bf8fc22e2f35a16b69", "metric_id": "9151e2ed1b0108e8d196c49931bee6b0", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_count", "anomaly_score": -0.5522099797, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 352.0, "min_metric_value": 292.158333807, "max_metric_value": 438.841666193, "training_avg": 365.5, "training_stddev": 24.447222064, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_count value is 352. The average for this metric is 365.5.", "is_anomalous": false}, {"value": 379.0, "average": 368.2, "min_value": 302.152289972, "max_value": 434.247710028, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "0c0a98870b2952a815eb6f0a34e1a6db", "metric_id": "98eedb3eea6213e4a19491451b591727", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_count", "anomaly_score": 0.4905544793, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 379.0, "min_metric_value": 302.152289972, "max_metric_value": 434.247710028, "training_avg": 368.2, "training_stddev": 22.015903343, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_count value is 379. The average for this metric is 368.2.", "is_anomalous": false}, {"value": 376.0, "average": 369.5, "min_value": 309.657707263, "max_value": 429.342292737, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "4a4d148ad55bbfa23ba8b89a19962014", "metric_id": "1c04a5d85e67032a11cad72314f36eaf", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_count", "anomaly_score": 0.3258564989, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 376.0, "min_metric_value": 309.657707263, "max_metric_value": 429.342292737, "training_avg": 369.5, "training_stddev": 19.947430912, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_count value is 376. The average for this metric is 369.5.", "is_anomalous": false}, {"value": 349.0, "average": 366.571428571, "min_value": 307.203327468, "max_value": 425.939529674, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "4c6600d75f71ecb731dc674b8ec3ab7a", "metric_id": "50e26bbf4a2570887a9125f1973adc71", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_count", "anomaly_score": -0.8879227183, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 349.0, "min_metric_value": 307.203327468, "max_metric_value": 425.939529674, "training_avg": 366.571428571, "training_stddev": 19.789367034, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_count value is 349. The average for this metric is 366.571.", "is_anomalous": false}, {"value": 383.0, "average": 368.625, "min_value": 310.964819385, "max_value": 426.285180615, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "6b0637daf524421628f2373ad2db768a", "metric_id": "4f7f7fae794d7c2bf2bb8b3ec6817ed2", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_count", "anomaly_score": 0.7479164918, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 383.0, "min_metric_value": 310.964819385, "max_metric_value": 426.285180615, "training_avg": 368.625, "training_stddev": 19.220060205, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_count value is 383. The average for this metric is 368.625.", "is_anomalous": false}, {"value": 340.0, "average": 365.444444444, "min_value": 304.382999981, "max_value": 426.505888908, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "a437488e45460da3beb4fab426d7b55b", "metric_id": "a2f43cf0a3d6d4a516a2f7a8b7d41565", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_count", "anomaly_score": -1.250106905, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 340.0, "min_metric_value": 304.382999981, "max_metric_value": 426.505888908, "training_avg": 365.444444444, "training_stddev": 20.353814821, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_count value is 340. The average for this metric is 365.444.", "is_anomalous": false}, {"value": 339.0, "average": 362.8, "min_value": 300.001910857, "max_value": 425.598089143, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "81e6c945f2708753b79ebc79247da7fc", "metric_id": "0ed79afa89dfc69e9638f82d485b5fbb", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_count", "anomaly_score": -1.136977271, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 339.0, "min_metric_value": 300.001910857, "max_metric_value": 425.598089143, "training_avg": 362.8, "training_stddev": 20.932696381, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_count value is 339. The average for this metric is 362.8.", "is_anomalous": false}, {"value": 370.0, "average": 363.454545455, "min_value": 303.524131261, "max_value": 423.384959648, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "6495259c37a81c0b234443029c4dafc2", "metric_id": "dc92ee951260b5e5fbe6f2e7fb19bbd6", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_count", "anomaly_score": 0.3276527269, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 370.0, "min_metric_value": 303.524131261, "max_metric_value": 423.384959648, "training_avg": 363.454545455, "training_stddev": 19.976804731, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_count value is 370. The average for this metric is 363.455.", "is_anomalous": false}, {"value": 367.0, "average": 363.75, "min_value": 306.526157226, "max_value": 420.973842774, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "1e51122a3abf3d33fb2133ca06c6d8a7", "metric_id": "26ca2827a080b2814d822ec0207392a2", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_count", "anomaly_score": 0.1703835242, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 367.0, "min_metric_value": 306.526157226, "max_metric_value": 420.973842774, "training_avg": 363.75, "training_stddev": 19.074614258, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_count value is 367. The average for this metric is 363.75.", "is_anomalous": false}, {"value": 370.0, "average": 364.230769231, "min_value": 309.196863598, "max_value": 419.264674864, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "01306ae1ef26d35648b2b3844bba5ed4", "metric_id": "207ffa39d464b48c63ad31d44e6d0859", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_count", "anomaly_score": 0.3144914414, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 370.0, "min_metric_value": 309.196863598, "max_metric_value": 419.264674864, "training_avg": 364.230769231, "training_stddev": 18.344635211, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_count value is 370. The average for this metric is 364.231.", "is_anomalous": false}, {"value": 389.0, "average": 366.0, "min_value": 309.51855308, "max_value": 422.48144692, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "ac0b43e68d85dafee129ae3b2f7d64bb", "metric_id": "be93d662ce44776b9d38adfa3f43d133", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_count", "anomaly_score": 1.221640092, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 389.0, "min_metric_value": 309.51855308, "max_metric_value": 422.48144692, "training_avg": 366.0, "training_stddev": 18.827148973, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_count value is 389. The average for this metric is 366.", "is_anomalous": false}, {"value": 357.0, "average": 365.4, "min_value": 310.52846171, "max_value": 420.27153829, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "3a616f5cdcc8e9c85f1ab8c3cf364606", "metric_id": "625c763e3210fb51e6e8543f02ebf705", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_count", "anomaly_score": -0.4592544839, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 357.0, "min_metric_value": 310.52846171, "max_metric_value": 420.27153829, "training_avg": 365.4, "training_stddev": 18.290512763, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_count value is 357. The average for this metric is 365.4.", "is_anomalous": false}, {"value": 352.0, "average": 364.5625, "min_value": 310.607310259, "max_value": 418.517689741, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "eb73fad78e396d46f1c39f6b7cd0b921", "metric_id": "5db68a3e278b693893703459d7ea0cf4", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_count", "anomaly_score": -0.6984962926, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 352.0, "min_metric_value": 310.607310259, "max_metric_value": 418.517689741, "training_avg": 364.5625, "training_stddev": 17.985063247, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_count value is 352. The average for this metric is 364.563.", "is_anomalous": false}, {"value": 364.0, "average": 364.529411765, "min_value": 312.285920754, "max_value": 416.772902775, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "7a542ae5a7106dd635ce4ff18157883f", "metric_id": "8d4408af70ed5193934bd0f309427871", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_count", "anomaly_score": -0.03040063486, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 364.0, "min_metric_value": 312.285920754, "max_metric_value": 416.772902775, "training_avg": 364.529411765, "training_stddev": 17.414497003, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_count value is 364. The average for this metric is 364.529.", "is_anomalous": false}, {"value": 372.0, "average": 364.944444444, "min_value": 313.986272439, "max_value": 415.90261645, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "5745510d42413e9e77983723a0551897", "metric_id": "0db65acfd42630392a34f0784fe49265", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_count", "anomaly_score": 0.415373351, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 372.0, "min_metric_value": 313.986272439, "max_metric_value": 415.90261645, "training_avg": 364.944444444, "training_stddev": 16.986057335, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_count value is 372. The average for this metric is 364.944.", "is_anomalous": false}, {"value": 352.0, "average": 364.263157895, "min_value": 313.945744356, "max_value": 414.580571434, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "d2242baccdc362fc7b5bffa31fa400ef", "metric_id": "c444d58f17ebd344de4a26a79c252255", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_count", "anomaly_score": -0.7311479485, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 352.0, "min_metric_value": 313.945744356, "max_metric_value": 414.580571434, "training_avg": 364.263157895, "training_stddev": 16.77247118, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_count value is 352. The average for this metric is 364.263.", "is_anomalous": false}, {"value": 355.0, "average": 363.8, "min_value": 314.431994166, "max_value": 413.168005834, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "d73fe750ed13f03d2450864faaf800a9", "metric_id": "f2c896cba1539126f4f112edf12c068c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_count", "anomaly_score": -0.5347592951, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 355.0, "min_metric_value": 314.431994166, "max_metric_value": 413.168005834, "training_avg": 363.8, "training_stddev": 16.456001945, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_count value is 355. The average for this metric is 363.8.", "is_anomalous": false}, {"value": 388.0, "average": 364.952380952, "min_value": 314.293437305, "max_value": 415.611324599, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "9f779476e5e68abd0065de08d62d217d", "metric_id": "221dc3d57cc8543e263f4f569e1960f5", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_count", "anomaly_score": 1.364869699, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 388.0, "min_metric_value": 314.293437305, "max_metric_value": 415.611324599, "training_avg": 364.952380952, "training_stddev": 16.886314549, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_count value is 388. The average for this metric is 364.952.", "is_anomalous": false}, {"value": 361.0, "average": 364.772727273, "min_value": 315.27007091, "max_value": 414.275383636, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "d6855087164b9a17ec88ace0caec3072", "metric_id": "d49e1321f81d45a82e193a60bfad867c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_count", "anomaly_score": -0.2286378681, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 361.0, "min_metric_value": 315.27007091, "max_metric_value": 414.275383636, "training_avg": 364.772727273, "training_stddev": 16.500885454, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_count value is 361. The average for this metric is 364.773.", "is_anomalous": false}, {"value": 361.0, "average": 364.608695652, "min_value": 316.186638156, "max_value": 413.030753148, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "b3f870c997a6824555e05d0933ebc66a", "metric_id": "fc6de8d69140cb65b654eb6169ad4808", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_count", "anomaly_score": -0.2235775908, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 361.0, "min_metric_value": 316.186638156, "max_metric_value": 413.030753148, "training_avg": 364.608695652, "training_stddev": 16.140685832, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_count value is 361. The average for this metric is 364.609.", "is_anomalous": false}, {"value": 364.0, "average": 364.583333333, "min_value": 317.224159942, "max_value": 411.942506725, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "45b028db73084a5632d29a93ac278567", "metric_id": "1a75031448a4fdb37f9497d1f8de4f53", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_count", "anomaly_score": -0.03695165846, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 364.0, "min_metric_value": 317.224159942, "max_metric_value": 411.942506725, "training_avg": 364.583333333, "training_stddev": 15.786391131, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_count value is 364. The average for this metric is 364.583.", "is_anomalous": false}, {"value": 357.0, "average": 364.28, "min_value": 317.695238543, "max_value": 410.864761457, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "2bbe686ab78810af740035cdeb6a40f7", "metric_id": "3e1bf9a956b4741b2c226d4bb75ba584", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_count", "anomaly_score": -0.4688228364, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 357.0, "min_metric_value": 317.695238543, "max_metric_value": 410.864761457, "training_avg": 364.28, "training_stddev": 15.528253819, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_count value is 357. The average for this metric is 364.28.", "is_anomalous": false}, {"value": 345.0, "average": 363.538461538, "min_value": 316.506492051, "max_value": 410.570431026, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "57760c0139dc35a835866d64b90d322a", "metric_id": "98876b594855cd32b2255549bf542475", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_count", "anomaly_score": -1.182501716, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 345.0, "min_metric_value": 316.506492051, "max_metric_value": 410.570431026, "training_avg": 363.538461538, "training_stddev": 15.677323162, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_count value is 345. The average for this metric is 363.538.", "is_anomalous": false}, {"value": 346.0, "average": 362.888888889, "min_value": 315.671714699, "max_value": 410.106063079, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "301ed246a46a57366db44cff84bcca84", "metric_id": "fda9d2e0a327868c50e6ed84e8ddce40", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_count", "anomaly_score": -1.073055886, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 346.0, "min_metric_value": 315.671714699, "max_metric_value": 410.106063079, "training_avg": 362.888888889, "training_stddev": 15.739058063, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_count value is 346. The average for this metric is 362.889.", "is_anomalous": false}, {"value": 346.0, "average": 362.285714286, "min_value": 314.972170468, "max_value": 409.599258103, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "b5b6492af25dfce3877302a84b37b4f5", "metric_id": "c997b0e16cc3bd4f41517986e9e72e72", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_count", "anomaly_score": -1.032624887, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 346.0, "min_metric_value": 314.972170468, "max_metric_value": 409.599258103, "training_avg": 362.285714286, "training_stddev": 15.771181273, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_count value is 346. The average for this metric is 362.286.", "is_anomalous": false}, {"value": 63.0, "average": 351.965517241, "min_value": 314.972170468, "max_value": 409.599258103, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "9ad3a8b88faf14da72a6e468accd497f", "metric_id": "62bb0101ab0b0da6aba16b41e3b7381f", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_count", "anomaly_score": -5.008636228, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 63.0, "min_metric_value": 178.885159185, "max_metric_value": 525.045875297, "training_avg": 351.965517241, "training_stddev": 57.693452685, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_count value is 63. The average for this metric is 351.966.", "is_anomalous": true}, {"value": 175.0, "average": 346.066666667, "min_value": 150.314641971, "max_value": 541.818691362, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "0fc4fbabeafb4dc700b7758c5a1b1f1b", "metric_id": "93d2befaf02267be88cc7d3e2474ff86", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_count", "anomaly_score": -2.62168425, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 175.0, "min_metric_value": 150.314641971, "max_metric_value": 541.818691362, "training_avg": 346.066666667, "training_stddev": 65.250674899, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_count value is 175. The average for this metric is 346.067.", "is_anomalous": false}], "result_description": "In column NULL_PERCENT_STR, the last missing_count value is 175. The average for this metric is 346.067."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "UPDATED_AT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('UPDATED_AT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column UPDATED_AT, the last null_percent value is 0. The average for this metric is 0.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('UPDATED_AT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Null Percent", "metrics": [{"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "f692c30e0fafa4cec8805e74359073ce", "metric_id": "46575bd90df9c1189dc6f39b0d48f15b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "2ed57db459957462b50a8a3cd5a6ccd2", "metric_id": "a13cc01e9c65e561bdfa2e987a8ec9d7", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "c2bd313fa90b37e76406db9cc3ffb4f2", "metric_id": "90c536bebfd0b5e0625af15e1576cc81", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "d363ece429db1781e970ce81ed70b535", "metric_id": "eaed05ef7f658c9e320c10c0ee2d9537", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "5f76cce7d57e926f76184df014efb226", "metric_id": "76fc94f6b1eba86f4d2237043c6ac704", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "a4d94255daf16d7ddfe56eedded698d5", "metric_id": "ffc4312bcfe325bd6fbefaa90b711a34", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "07d5c8dda2202f4da687dff7fca84dd3", "metric_id": "a9813722ba54c400142329172105e395", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "72dd5a25c9fe0c96516360d5968debe0", "metric_id": "b3799456099aae995c53db8166070fb7", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "73d4b15fdf68c1c62ca883d2089c455f", "metric_id": "958c1c0337e7401ba289e9636617ff6e", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "c6e144703f0216717a422ee89c1d18e5", "metric_id": "db56116909387c12a9faf5d0c3a6e81f", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "5fa08ce3b804980adc26be2c0141a663", "metric_id": "cc80654d41a2cfca5cfcf92d6033e6ed", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "a01fb295fd4deb4070f5fe3ef3f25b74", "metric_id": "f501addf7f2d1d586669d3e4d127d1e2", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "177b29474f44f3dc00dd7273cf2da8bc", "metric_id": "e30dab8b91a01ca6be616d2f93b32c29", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "5c646649b4798480f85728c2d7701e6c", "metric_id": "1a93d45753a4c3202d420aa99eeb0c17", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "03d138c44fa9f319b1cbe685fe63ef89", "metric_id": "62380712fe494c672a6f3478b03fce76", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "9afac79b5d9f8c69d1c5bec8045e6d4a", "metric_id": "4ba94b46f28586b34896b8e5dff09e03", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "0425b075d50f5ff83760f913013b66d6", "metric_id": "d43967e44b64ed8f6c8e48e057fb452d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "40f9e96aefd78f2b47a8b3d74bef4b74", "metric_id": "8c106d74274fae30aff0b1893e4b1ebf", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "12339ae32546513b43cda639cdd775d8", "metric_id": "56cd1722a4635f4ae4134b4e9d1700ed", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "f2fb3a690b95035f1f855c1b437ea6ed", "metric_id": "873a7d5848e32f6b91dc7afcbf4f3698", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "1bd91fec8395e7a58eb56fcc4cca3936", "metric_id": "fcce1fc8e911190e0944fac2860f0e0e", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "5a6f93266fadadb47c84ed99b7be6767", "metric_id": "cca134edb6cb66d98e3046870206852d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "a572ba20e4af719dbe80cabd37bda9d0", "metric_id": "dcb6f74129606fc129991f69d18be253", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "62f9c6824ae25fde0498ea9e430ce5a0", "metric_id": "a459641e47f7030d7abe2af6b6800a42", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "786f475f3b8f9bfe974df9bf3da87a1c", "metric_id": "2effa046a3fed6529c25c8a34113d2c0", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "b861c36c31572b8be3919dcbc484198f", "metric_id": "ddbd26bd68dd9f74b9d941ac75ff7c81", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "fa3c4cb09b6796a82d3fe663f3b658b3", "metric_id": "d999e8735f1065f0204bd0237a4a4342", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "7bf3e8ad9a2e48761b08c95937056bf7", "metric_id": "edaeb0a7a5239c3cc704a21f0dd7ca0d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "503dfb2f9149f53d98b724bd8a8e2d68", "metric_id": "43856df870b83793d6ae7a240dbf34dc", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "UPDATED_AT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column UPDATED_AT, the last null_percent value is 0. The average for this metric is 0.", "is_anomalous": false}], "result_description": "In column UPDATED_AT, the last null_percent value is 0. The average for this metric is 0."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "max", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('max' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_FLOAT, the last max value is 8.87. The average for this metric is 8.893.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('max' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Max", "metrics": [{"value": 8.894378244, "average": 8.895872597, "min_value": 8.889532592, "max_value": 8.902212602, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "0403542e6ce532102620e4ee246161b0", "metric_id": "84d29177e39e53ca87e1866d660cd7c6", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "max", "anomaly_score": -0.7071067824, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 8.894378244, "min_metric_value": 8.889532592, "max_metric_value": 8.902212602, "training_avg": 8.895872597, "training_stddev": 0.002113334902, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last max value is 8.894. The average for this metric is 8.896.", "is_anomalous": false}, {"value": 8.884302489, "average": 8.892015894, "min_value": 8.871480558, "max_value": 8.912551231, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "541c10eba3175173420dc9f53621421c", "metric_id": "f67f468e6cf552590ed3eb3c0abbd02c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "max", "anomaly_score": -1.126848664, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 8.884302489, "min_metric_value": 8.871480558, "max_metric_value": 8.912551231, "training_avg": 8.892015894, "training_stddev": 0.006845112072, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last max value is 8.884. The average for this metric is 8.892.", "is_anomalous": false}, {"value": 8.896242573, "average": 8.893072564, "min_value": 8.875146907, "max_value": 8.910998221, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "b841a683f151c6e1a2149206b93fdd1b", "metric_id": "faa97725b5611d6d3dcd6235df0ed85f", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "max", "anomaly_score": 0.530525964, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 8.896242573, "min_metric_value": 8.875146907, "max_metric_value": 8.910998221, "training_avg": 8.893072564, "training_stddev": 0.005975219003, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last max value is 8.896. The average for this metric is 8.893.", "is_anomalous": false}, {"value": 8.899758667, "average": 8.894409784, "min_value": 8.876480367, "max_value": 8.912339202, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "9c640a449b24d44f42154a1111a64450", "metric_id": "ab0dcedbf8f7c6f870fc7f098370ee94", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "max", "anomaly_score": 0.8949898018, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 8.899758667, "min_metric_value": 8.876480367, "max_metric_value": 8.912339202, "training_avg": 8.894409784, "training_stddev": 0.005976472632, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last max value is 8.9. The average for this metric is 8.894.", "is_anomalous": false}, {"value": 8.893601268, "average": 8.894275032, "min_value": 8.87820793, "max_value": 8.910342134, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "bdf99663d9e9916844f354f5d1ac5d36", "metric_id": "edf9558175fdbb77bdf78aee5d74f742", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "max", "anomaly_score": -0.1258030212, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 8.893601268, "min_metric_value": 8.87820793, "max_metric_value": 8.910342134, "training_avg": 8.894275032, "training_stddev": 0.005355700682, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last max value is 8.894. The average for this metric is 8.894.", "is_anomalous": false}, {"value": 8.890926226, "average": 8.893796631, "min_value": 8.878645884, "max_value": 8.908947377, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "667216b77eec1aef52d3b21d9c052094", "metric_id": "e841bc59d1e6a3540d3adaed7d3b5748", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "max", "anomaly_score": -0.5683690668, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 8.890926226, "min_metric_value": 8.878645884, "max_metric_value": 8.908947377, "training_avg": 8.893796631, "training_stddev": 0.005050248828, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last max value is 8.891. The average for this metric is 8.894.", "is_anomalous": false}, {"value": 8.893223323, "average": 8.893724967, "min_value": 8.879684927, "max_value": 8.907765008, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "49599b3a8597d3549d982dd45236c619", "metric_id": "ad512f2e85785ff639f73c4746038809", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "max", "anomaly_score": -0.107188592, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 8.893223323, "min_metric_value": 8.879684927, "max_metric_value": 8.907765008, "training_avg": 8.893724967, "training_stddev": 0.004680013365, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last max value is 8.893. The average for this metric is 8.894.", "is_anomalous": false}, {"value": 8.899532938, "average": 8.894370298, "min_value": 8.880010113, "max_value": 8.908730482, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "27ba33066a70870fb91221ee9e8bfdda", "metric_id": "c27bea4ef53b7e7d1c2673082c6fb6a7", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "max", "anomaly_score": 1.078532151, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 8.899532938, "min_metric_value": 8.880010113, "max_metric_value": 8.908730482, "training_avg": 8.894370298, "training_stddev": 0.004786728231, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last max value is 8.9. The average for this metric is 8.894.", "is_anomalous": false}, {"value": 8.896625976, "average": 8.894595865, "min_value": 8.880888881, "max_value": 8.90830285, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "ff1992239990e571ca72babf90e70fe9", "metric_id": "ec8644e1d17491e6aa574d43dfcb6ea5", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "max", "anomaly_score": 0.4443233578, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 8.896625976, "min_metric_value": 8.880888881, "max_metric_value": 8.90830285, "training_avg": 8.894595865, "training_stddev": 0.004568994737, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last max value is 8.897. The average for this metric is 8.895.", "is_anomalous": false}, {"value": 8.893867346, "average": 8.894529636, "min_value": 8.881509363, "max_value": 8.90754991, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "3d67c6def1f8586316ecb2b1ab376e43", "metric_id": "a8db63eab93fa69a6001f8525d809cd8", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "max", "anomaly_score": -0.1525982326, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 8.893867346, "min_metric_value": 8.881509363, "max_metric_value": 8.90754991, "training_avg": 8.894529636, "training_stddev": 0.004340091097, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last max value is 8.894. The average for this metric is 8.895.", "is_anomalous": false}, {"value": 8.893990657, "average": 8.894484721, "min_value": 8.882061606, "max_value": 8.906907837, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "fe5a25f607306fb6c626b3b50b469f6e", "metric_id": "dcde3cc813ab9300c1177ab13a8e772d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "max", "anomaly_score": -0.1193093022, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 8.893990657, "min_metric_value": 8.882061606, "max_metric_value": 8.906907837, "training_avg": 8.894484721, "training_stddev": 0.004141038495, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last max value is 8.894. The average for this metric is 8.894.", "is_anomalous": false}, {"value": 8.890561618, "average": 8.894182944, "min_value": 8.881848935, "max_value": 8.906516953, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "472d0eaa6d978059c4101656161b11fc", "metric_id": "e6eca9016425929b55804f73dc2939c7", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "max", "anomaly_score": -0.880814925, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 8.890561618, "min_metric_value": 8.881848935, "max_metric_value": 8.906516953, "training_avg": 8.894182944, "training_stddev": 0.004111336326, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last max value is 8.891. The average for this metric is 8.894.", "is_anomalous": false}, {"value": 8.895889979, "average": 8.894304875, "min_value": 8.882375965, "max_value": 8.906233786, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "d7a06049569c54b518b7751be2880ef5", "metric_id": "d0228a5a9b6bb4a6fc9251352be29c5b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "max", "anomaly_score": 0.3986374844, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 8.895889979, "min_metric_value": 8.882375965, "max_metric_value": 8.906233786, "training_avg": 8.894304875, "training_stddev": 0.003976303497, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last max value is 8.896. The average for this metric is 8.894.", "is_anomalous": false}, {"value": 8.899168556, "average": 8.894629121, "min_value": 8.882532512, "max_value": 8.906725729, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "a93967329bd95d72d0749dce593776a8", "metric_id": "90b898cf7606bf1de23b25da8106b7fe", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "max", "anomaly_score": 1.125795473, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 8.899168556, "min_metric_value": 8.882532512, "max_metric_value": 8.906725729, "training_avg": 8.894629121, "training_stddev": 0.004032202789, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last max value is 8.899. The average for this metric is 8.895.", "is_anomalous": false}, {"value": 8.897662193, "average": 8.894818688, "min_value": 8.882912913, "max_value": 8.906724463, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "b9357f1f63f6e14590c80ea1263be29a", "metric_id": "c034b45bca4ed8ec5c53259025a01531", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "max", "anomaly_score": 0.7165024723, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 8.897662193, "min_metric_value": 8.882912913, "max_metric_value": 8.906724463, "training_avg": 8.894818688, "training_stddev": 0.003968591611, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last max value is 8.898. The average for this metric is 8.895.", "is_anomalous": false}, {"value": 8.895621286, "average": 8.894865899, "min_value": 8.8833234, "max_value": 8.906408399, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "7000499dee84a669779437a55e6de627", "metric_id": "08c809d7cb913757ef00a9313944d94b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "max", "anomaly_score": 0.1963318482, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 8.895621286, "min_metric_value": 8.8833234, "max_metric_value": 8.906408399, "training_avg": 8.894865899, "training_stddev": 0.003847499697, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last max value is 8.896. The average for this metric is 8.895.", "is_anomalous": false}, {"value": 8.892028049, "average": 8.894708241, "min_value": 8.883331995, "max_value": 8.906084487, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "15d7643c0f1add36795141684b599b8f", "metric_id": "d66c026e561f0611847e2925a309bec7", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "max", "anomaly_score": -0.7067864258, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 8.892028049, "min_metric_value": 8.883331995, "max_metric_value": 8.906084487, "training_avg": 8.894708241, "training_stddev": 0.003792081891, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last max value is 8.892. The average for this metric is 8.895.", "is_anomalous": false}, {"value": 8.880456629, "average": 8.893958156, "min_value": 8.879178498, "max_value": 8.908737814, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "7e3274cffbfef0a8f9d789a483cf2860", "metric_id": "5f1f403ef623adc6bd8bba9c2ac152c5", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "max", "anomaly_score": -2.74056287, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 8.880456629, "min_metric_value": 8.879178498, "max_metric_value": 8.908737814, "training_avg": 8.893958156, "training_stddev": 0.004926552628, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last max value is 8.88. The average for this metric is 8.894.", "is_anomalous": false}, {"value": 8.897795821, "average": 8.894150039, "min_value": 8.87953604, "max_value": 8.908764039, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "40b3f45048520dd43d7b7817a0807544", "metric_id": "969b62a1dfe7ce571fe0f48a055c27af", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "max", "anomaly_score": 0.7484154746, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 8.897795821, "min_metric_value": 8.87953604, "max_metric_value": 8.908764039, "training_avg": 8.894150039, "training_stddev": 0.004871333217, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last max value is 8.898. The average for this metric is 8.894.", "is_anomalous": false}, {"value": 8.889753953, "average": 8.893940702, "min_value": 8.879408912, "max_value": 8.908472492, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "b4d3afd5e7b6c48d9020c8a70e061fbd", "metric_id": "4fe4f69201c60805cbf2253d3a10e3e8", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "max", "anomaly_score": -0.8643288575, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 8.889753953, "min_metric_value": 8.879408912, "max_metric_value": 8.908472492, "training_avg": 8.893940702, "training_stddev": 0.004843930043, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last max value is 8.89. The average for this metric is 8.894.", "is_anomalous": false}, {"value": 8.899833844, "average": 8.894208572, "min_value": 8.879534635, "max_value": 8.90888251, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "84beb65c07a4c22f30811361bb7b744d", "metric_id": "e5ed082d77e0d628d663113dcaf102f4", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "max", "anomaly_score": 1.150053697, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 8.899833844, "min_metric_value": 8.879534635, "max_metric_value": 8.90888251, "training_avg": 8.894208572, "training_stddev": 0.004891312475, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last max value is 8.9. The average for this metric is 8.894.", "is_anomalous": false}, {"value": 8.899232255, "average": 8.894426993, "min_value": 8.879750057, "max_value": 8.90910393, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "f66fa9a7589b6dc404d5b35b8f89fdd2", "metric_id": "5ea2d2d69085812580484fbc13dff2cc", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "max", "anomaly_score": 0.9822068105, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 8.899232255, "min_metric_value": 8.879750057, "max_metric_value": 8.90910393, "training_avg": 8.894426993, "training_stddev": 0.004892312191, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last max value is 8.899. The average for this metric is 8.894.", "is_anomalous": false}, {"value": 8.886621221, "average": 8.894101753, "min_value": 8.87897246, "max_value": 8.909231045, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "a65647bfbda00190907b5531e24ec630", "metric_id": "3f7dd1ac143218fe63d1b74e2cb0a0e0", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "max", "anomaly_score": -1.48332087, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 8.886621221, "min_metric_value": 8.87897246, "max_metric_value": 8.909231045, "training_avg": 8.894101753, "training_stddev": 0.005043097455, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last max value is 8.887. The average for this metric is 8.894.", "is_anomalous": false}, {"value": 8.893737796, "average": 8.894087194, "min_value": 8.879274839, "max_value": 8.908899549, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "e339b40294ec479e0114d975a5d8e2ee", "metric_id": "b12bd9ac7c00cc97f58cc33bb9d1a8ea", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "max", "anomaly_score": -0.07076481762, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 8.893737796, "min_metric_value": 8.879274839, "max_metric_value": 8.908899549, "training_avg": 8.894087194, "training_stddev": 0.00493745169, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last max value is 8.894. The average for this metric is 8.894.", "is_anomalous": false}, {"value": 8.899798244, "average": 8.89430685, "min_value": 8.879409877, "max_value": 8.909203823, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "e67710dca3a27c0048eb4c4e4c2f056b", "metric_id": "f20260be5e73bc97fe2a20da8b0247cb", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "max", "anomaly_score": 1.105874502, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 8.899798244, "min_metric_value": 8.879409877, "max_metric_value": 8.909203823, "training_avg": 8.89430685, "training_stddev": 0.004965657773, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last max value is 8.9. The average for this metric is 8.894.", "is_anomalous": false}, {"value": 8.899436335, "average": 8.894496831, "min_value": 8.879591967, "max_value": 8.909401695, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "37e6dd3ad6f21643352aa324c2222ff8", "metric_id": "d925b86b02864393a234d258d7e12f86", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "max", "anomaly_score": 0.9942065039, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 8.899436335, "min_metric_value": 8.879591967, "max_metric_value": 8.909401695, "training_avg": 8.894496831, "training_stddev": 0.004968288059, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last max value is 8.899. The average for this metric is 8.894.", "is_anomalous": false}, {"value": 8.888920662, "average": 8.894297682, "min_value": 8.879333679, "max_value": 8.909261686, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "e5937a4ea37cd73c64d697e6a6fb5deb", "metric_id": "6a7d564a13daf7454163c2ddb6acba82", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "max", "anomaly_score": -1.077990951, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 8.888920662, "min_metric_value": 8.879333679, "max_metric_value": 8.909261686, "training_avg": 8.894297682, "training_stddev": 0.004988001217, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last max value is 8.889. The average for this metric is 8.894.", "is_anomalous": false}, {"value": 8.894374903, "average": 8.894300345, "min_value": 8.879605922, "max_value": 8.908994768, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "67e55d73129b30d0ad48121daa8a39e9", "metric_id": "b015a8e20ac87da5b6d3bd5d775ed149", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "max", "anomaly_score": 0.01522168753, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 8.894374903, "min_metric_value": 8.879605922, "max_metric_value": 8.908994768, "training_avg": 8.894300345, "training_stddev": 0.00489814095, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last max value is 8.894. The average for this metric is 8.894.", "is_anomalous": false}, {"value": 8.870209378, "average": 8.893497313, "min_value": 8.879605922, "max_value": 8.908994768, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "e2eddd1801d24b1c182a23dc0c522869", "metric_id": "394b72e39fb3ea27366114c514f90ded", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "max", "anomaly_score": -3.571773138, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 8.870209378, "min_metric_value": 8.873937335, "max_metric_value": 8.913057291, "training_avg": 8.893497313, "training_stddev": 0.006519992713, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last max value is 8.87. The average for this metric is 8.893.", "is_anomalous": true}], "result_description": "In column NULL_COUNT_FLOAT, the last max value is 8.87. The average for this metric is 8.893."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_STR, the last null_count value is 240. The average for this metric is 58.7.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Null Count", "metrics": [{"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "424991ae0f7ae8d938f1d90500919f72", "metric_id": "8c765cb6dd8b43d9a132592f2e971f0d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "28d9133d70e42f757ce2293889d0b4ea", "metric_id": "af3270c6c19da84436c79aba0ddfb35b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "63db5e75a856e00604c618eacc825bc7", "metric_id": "9fc9ccc8e8903b04dcec4306e9a0dfbb", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "a5f22eb86430cb323ca4d3dd73955648", "metric_id": "b60a4f2bcb57621d392fbd0bbdbd65cc", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "593604166d15f66e6402dee584563cce", "metric_id": "c4cbf96244348c0cab1017c0c0b206a3", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "1fe7a79a2b9b28c2c76dbf0434426f33", "metric_id": "e5cb098518dcc39cb9c1da4dd9c22c77", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "2419abfe5cea93bf0e725b1a8df62fe4", "metric_id": "37b86b0bb737b89a26084423929d7d57", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "579d038870c58adf870df2a2592ae8e3", "metric_id": "6012dafdbec8447eadd4a5032a152a8c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "0e583be1ac96fe57954e19d82439b5aa", "metric_id": "c434c3f5dcd9b15055a78b855a0fa2a0", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "1d584b83ba1f9e069af49151664de7b9", "metric_id": "a97aa6281fffc1a372faf89d737e828b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "20fa49044a7786a03ab5bd0db73f2cce", "metric_id": "a26a811962f17a30c219276ef3805e95", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "36b4a78ce0a009e88917ca5ba4578ae6", "metric_id": "c095da39f0a1ce1b4423d89137c9689f", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "16539f86771d8f2be461dd7375936bac", "metric_id": "17f4e2fc6924a790bf6b7565bdbb145e", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "eada1a6666792079234b9b6267da7ae5", "metric_id": "09738f38cca5b9d2b02fe9b480e8fe2f", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "305a3094991d7e78db431a3cdefcebf6", "metric_id": "f7a3b4b2916ad9e4f7957e195e0c71ab", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "e3fd2c71f26b239124206807ee38e766", "metric_id": "393fd85c00dacc9f2e18fab9e1e2b234", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "f89293c0acbc49f99895eb5e3a50b679", "metric_id": "4eb2504385e64c017ff41c9e03c40198", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "8bc171d9fb7ef582d895d9dac1d22d8a", "metric_id": "0a782285d76fd9b32be7434a8dbe02fb", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "a1d93ed7ff8e1f24f97dff12c5cb73e0", "metric_id": "3b3eb7acf26898f6aae19287dd3eeb0d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "41f80e058ebd2bce6a0c90f547a9f48a", "metric_id": "17f3a32d79771073d4a2f3b9a8cba99c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "c65e446eb175e5a9b64b72ccf4415de3", "metric_id": "32b72f6fc02f4dc75c88f6609d64e98a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "44925c22cc6bcb5a554ddc7ea537e6c0", "metric_id": "5eb9d13bf756b488179b58854a125dcb", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "0a4315920f21126fdd1d2838d4e65af5", "metric_id": "6fb0590e43dec6e8c847554dad092a6b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "26fd3a6de9951ebfbbeaa9fce243c414", "metric_id": "c2d47f3327052483c3d4c824f6721dd6", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "18de5e9f73d30c4c85035a6205d64626", "metric_id": "0d93c346046ebf692061d7a8946aa28d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "2a89f74505cdff76ba6c81f734c3a8df", "metric_id": "307aafdc1a3227d48ca05f0f917c5501", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "83246207eb402925fe17d538ccff4183", "metric_id": "e151d703a56c4a5ff5f2eb7bc2563887", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 9.0, "average": 52.448275862, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "9f8bd686cbcebdc875d93a22acf64dd7", "metric_id": "39ae745105a2cfb5ea3ab168655785e4", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_count", "anomaly_score": -5.199469469, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 9.0, "min_metric_value": 27.379405208, "max_metric_value": 77.517146516, "training_avg": 52.448275862, "training_stddev": 8.356290218, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_count value is 9. The average for this metric is 52.448.", "is_anomalous": true}, {"value": 240.0, "average": 58.7, "min_value": 27.379405208, "max_value": 77.517146516, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "9316d42db48de7e85afd2b436711d913", "metric_id": "3c0d3d5a017d75ca9af5d51b956e3326", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_count", "anomaly_score": 5.148695731, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 240.0, "min_metric_value": -46.938404067, "max_metric_value": 164.338404067, "training_avg": 58.7, "training_stddev": 35.212801356, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_count value is 240. The average for this metric is 58.7.", "is_anomalous": true}], "result_description": "In column NULL_COUNT_STR, the last null_count value is 240. The average for this metric is 58.7."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "average", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_FLOAT, the last average value is 5.022. The average for this metric is 5.043.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Average", "metrics": [{"value": 5.016173703, "average": 5.024629855, "min_value": 4.988753441, "max_value": 5.060506269, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "672c1f151458dcfefec711c4c260e387", "metric_id": "6513e6fff43c6ff8391642a15870eeb4", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "average", "anomaly_score": -0.7071067812, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 5.016173703, "min_metric_value": 4.988753441, "max_metric_value": 5.060506269, "training_avg": 5.024629855, "training_stddev": 0.01195880469, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last average value is 5.016. The average for this metric is 5.025.", "is_anomalous": false}, {"value": 5.112147927, "average": 5.053802545, "min_value": 4.900108703, "max_value": 5.207496388, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "c79a2674cc1ebe205a0f54c0e0922890", "metric_id": "75da0f8c172d7e897423d7391cb9f8d9", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "average", "anomaly_score": 1.138862434, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 5.112147927, "min_metric_value": 4.900108703, "max_metric_value": 5.207496388, "training_avg": 5.053802545, "training_stddev": 0.05123128087, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last average value is 5.112. The average for this metric is 5.054.", "is_anomalous": false}, {"value": 5.089992117, "average": 5.062849938, "min_value": 4.926121535, "max_value": 5.199578342, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "9865639ecd0a9a93df2399507c04eadb", "metric_id": "9523dc9128f306fd6cc1765b41980076", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "average", "anomaly_score": 0.5955349064, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 5.089992117, "min_metric_value": 4.926121535, "max_metric_value": 5.199578342, "training_avg": 5.062849938, "training_stddev": 0.04557613455, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last average value is 5.09. The average for this metric is 5.063.", "is_anomalous": false}, {"value": 5.100865825, "average": 5.070453116, "min_value": 4.941525361, "max_value": 5.199380871, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "f66e7bca38daa07a06bb29e9ce61e737", "metric_id": "f31e6a1e8507977193a43df1d877d4df", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "average", "anomaly_score": 0.7076686256, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 5.100865825, "min_metric_value": 4.941525361, "max_metric_value": 5.199380871, "training_avg": 5.070453116, "training_stddev": 0.04297591829, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last average value is 5.101. The average for this metric is 5.07.", "is_anomalous": false}, {"value": 4.978631706, "average": 5.055149547, "min_value": 4.894076117, "max_value": 5.216222977, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "07d9b0735f40634b348611b8b070a27c", "metric_id": "6e24caf0827323159fc627b64a6fe648", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "average", "anomaly_score": -1.425148303, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 4.978631706, "min_metric_value": 4.894076117, "max_metric_value": 5.216222977, "training_avg": 5.055149547, "training_stddev": 0.05369114329, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last average value is 4.979. The average for this metric is 5.055.", "is_anomalous": false}, {"value": 5.02471199, "average": 5.050801325, "min_value": 4.899765949, "max_value": 5.201836701, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "366d24f2d3245e64a8c35cfcba0a3940", "metric_id": "0cbf9fe90563993abbc43e96a5df29a4", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "average", "anomaly_score": -0.5182097414, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 5.02471199, "min_metric_value": 4.899765949, "max_metric_value": 5.201836701, "training_avg": 5.050801325, "training_stddev": 0.05034512545, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last average value is 5.025. The average for this metric is 5.051.", "is_anomalous": false}, {"value": 5.046123423, "average": 5.050216587, "min_value": 4.910297, "max_value": 5.190136174, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "3fa5a9cc273cb559797ddf2a2a47d9bb", "metric_id": "7aec21f5ead6511bd80aaa1bd7d0c268", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "average", "anomaly_score": -0.08776106584, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 5.046123423, "min_metric_value": 4.910297, "max_metric_value": 5.190136174, "training_avg": 5.050216587, "training_stddev": 0.04663986241, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last average value is 5.046. The average for this metric is 5.05.", "is_anomalous": false}, {"value": 5.121247259, "average": 5.058108884, "min_value": 4.909193939, "max_value": 5.207023829, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "e7f35bd9b80a61ea29c3b60629e90794", "metric_id": "a2e28351eb664be627eaefbdbfa4ad16", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "average", "anomaly_score": 1.271968543, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 5.121247259, "min_metric_value": 4.909193939, "max_metric_value": 5.207023829, "training_avg": 5.058108884, "training_stddev": 0.04963831509, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last average value is 5.121. The average for this metric is 5.058.", "is_anomalous": false}, {"value": 5.09369577, "average": 5.061667573, "min_value": 4.917267149, "max_value": 5.206067997, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "0a85d87390200dc44720a7b2136d01b3", "metric_id": "797e28848ee035f8f94820aa91b39ef8", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "average", "anomaly_score": 0.6654038062, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 5.09369577, "min_metric_value": 4.917267149, "max_metric_value": 5.206067997, "training_avg": 5.061667573, "training_stddev": 0.0481334746, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last average value is 5.094. The average for this metric is 5.062.", "is_anomalous": false}, {"value": 4.954259416, "average": 5.051903195, "min_value": 4.883958863, "max_value": 5.219847527, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "18c5b817051aab675f68e97a53df5d41", "metric_id": "353840eaa39d782ed4de0b885eced0a8", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "average", "anomaly_score": -1.744216868, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 4.954259416, "min_metric_value": 4.883958863, "max_metric_value": 5.219847527, "training_avg": 5.051903195, "training_stddev": 0.05598144398, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last average value is 4.954. The average for this metric is 5.052.", "is_anomalous": false}, {"value": 5.029151412, "average": 5.050007213, "min_value": 4.888670877, "max_value": 5.211343549, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "d4acec21f4f690f60c8e7a9e496e5b50", "metric_id": "0b423c7dcf2f857a08ffcb159bad6058", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "average", "anomaly_score": -0.3878072592, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 5.029151412, "min_metric_value": 4.888670877, "max_metric_value": 5.211343549, "training_avg": 5.050007213, "training_stddev": 0.05377877864, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last average value is 5.029. The average for this metric is 5.05.", "is_anomalous": false}, {"value": 5.002136591, "average": 5.046324857, "min_value": 4.886804372, "max_value": 5.205845343, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "a03d1f755f0ff01e1b536ede733d294a", "metric_id": "b79e66f69ff141375ed35a74d025f579", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "average", "anomaly_score": -0.8310205338, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 5.002136591, "min_metric_value": 4.886804372, "max_metric_value": 5.205845343, "training_avg": 5.046324857, "training_stddev": 0.05317349523, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last average value is 5.002. The average for this metric is 5.046.", "is_anomalous": false}, {"value": 5.018897332, "average": 5.044365748, "min_value": 4.889533769, "max_value": 5.199197728, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "1bfb28d017e8f2448ab82e8502a3f22b", "metric_id": "e563f27896e4a020c996bef78db3ef56", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "average", "anomaly_score": -0.4934720182, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 5.018897332, "min_metric_value": 4.889533769, "max_metric_value": 5.199197728, "training_avg": 5.044365748, "training_stddev": 0.05161065989, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last average value is 5.019. The average for this metric is 5.044.", "is_anomalous": false}, {"value": 5.012637384, "average": 5.042250524, "min_value": 4.891040067, "max_value": 5.193460981, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "4309b4c17750c94668c4c722b774eaa6", "metric_id": "bc991ccda9f55658f16830b77e82067d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "average", "anomaly_score": -0.5875216708, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 5.012637384, "min_metric_value": 4.891040067, "max_metric_value": 5.193460981, "training_avg": 5.042250524, "training_stddev": 0.05040348577, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last average value is 5.013. The average for this metric is 5.042.", "is_anomalous": false}, {"value": 5.028384155, "average": 5.041383876, "min_value": 4.894930979, "max_value": 5.187836773, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "e9170307d9fc06435ecc3b7e54e90c30", "metric_id": "95d32f690a33d888874516e9d20cd374", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "average", "anomaly_score": -0.2662915197, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 5.028384155, "min_metric_value": 4.894930979, "max_metric_value": 5.187836773, "training_avg": 5.041383876, "training_stddev": 0.04881763222, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last average value is 5.028. The average for this metric is 5.041.", "is_anomalous": false}, {"value": 4.989601355, "average": 5.038337845, "min_value": 4.891615308, "max_value": 5.185060383, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "3ebdba7b2c4f1db89cd8998ca007e9c0", "metric_id": "9713311f3155c38de1f553a168ae0d70", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "average", "anomaly_score": -0.9965031542, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 4.989601355, "min_metric_value": 4.891615308, "max_metric_value": 5.185060383, "training_avg": 5.038337845, "training_stddev": 0.04890751258, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last average value is 4.99. The average for this metric is 5.038.", "is_anomalous": false}, {"value": 5.124662365, "average": 5.043133652, "min_value": 4.888255827, "max_value": 5.198011477, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "3e485b79d180220a8b70fb9bea9bafd6", "metric_id": "bf341fc22e5b4e5eee83adb46d73e4ce", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "average", "anomaly_score": 1.579219868, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 5.124662365, "min_metric_value": 4.888255827, "max_metric_value": 5.198011477, "training_avg": 5.043133652, "training_stddev": 0.05162594169, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last average value is 5.125. The average for this metric is 5.043.", "is_anomalous": false}, {"value": 5.077896004, "average": 5.04496325, "min_value": 4.892559405, "max_value": 5.197367094, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "55994393a410ffe4e1be4ffa5fd4a12a", "metric_id": "65de2772c8064d1ae83eb9e2095357e2", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "average", "anomaly_score": 0.6482662255, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 5.077896004, "min_metric_value": 4.892559405, "max_metric_value": 5.197367094, "training_avg": 5.04496325, "training_stddev": 0.05080128147, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last average value is 5.078. The average for this metric is 5.045.", "is_anomalous": false}, {"value": 5.128988863, "average": 5.04916453, "min_value": 4.890477422, "max_value": 5.207851639, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "a9545c9a7f2ff8446b5e0fa76e0fc8cb", "metric_id": "c116b0025f65cc7d9e75b52aec4e8d3b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "average", "anomaly_score": 1.50908918, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 5.128988863, "min_metric_value": 4.890477422, "max_metric_value": 5.207851639, "training_avg": 5.04916453, "training_stddev": 0.05289570279, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last average value is 5.129. The average for this metric is 5.049.", "is_anomalous": false}, {"value": 4.96364677, "average": 5.045092256, "min_value": 4.88060279, "max_value": 5.209581722, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "b259e41f4fd8f5abaff89c651e89165d", "metric_id": "2be458bcbea107f36b2b1a660203fa77", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "average", "anomaly_score": -1.48542313, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 4.96364677, "min_metric_value": 4.88060279, "max_metric_value": 5.209581722, "training_avg": 5.045092256, "training_stddev": 0.05482982201, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last average value is 4.964. The average for this metric is 5.045.", "is_anomalous": false}, {"value": 5.045507413, "average": 5.045111127, "min_value": 4.884585625, "max_value": 5.205636628, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "cd30e316d33a8d11cb0c873742348a48", "metric_id": "9a6c9f53f8c8497d8afaa9e8e8445d3d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "average", "anomaly_score": 0.007406045216, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 5.045507413, "min_metric_value": 4.884585625, "max_metric_value": 5.205636628, "training_avg": 5.045111127, "training_stddev": 0.05350850051, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last average value is 5.046. The average for this metric is 5.045.", "is_anomalous": false}, {"value": 5.071433069, "average": 5.046255559, "min_value": 4.888558834, "max_value": 5.203952284, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "5e43dd003b17ddcea7f3e06fe54c3b30", "metric_id": "68585e54984ba7537296919807710a0e", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "average", "anomaly_score": 0.4789733612, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 5.071433069, "min_metric_value": 4.888558834, "max_metric_value": 5.203952284, "training_avg": 5.046255559, "training_stddev": 0.05256557512, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last average value is 5.071. The average for this metric is 5.046.", "is_anomalous": false}, {"value": 5.059122206, "average": 5.046791669, "min_value": 4.892360098, "max_value": 5.20122324, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "77a235a94d866596725a0fdf418a2667", "metric_id": "744dbf5e6dc857b733b01c9588253397", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "average", "anomaly_score": 0.2395339975, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 5.059122206, "min_metric_value": 4.892360098, "max_metric_value": 5.20122324, "training_avg": 5.046791669, "training_stddev": 0.05147719031, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last average value is 5.059. The average for this metric is 5.047.", "is_anomalous": false}, {"value": 5.051525972, "average": 5.046981041, "min_value": 4.895774342, "max_value": 5.198187741, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "67aa4200ebd03490f45e34cc3ec7b09e", "metric_id": "e4bbfcf315c0d40b45c7615376ab023d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "average", "anomaly_score": 0.09017319919, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 5.051525972, "min_metric_value": 4.895774342, "max_metric_value": 5.198187741, "training_avg": 5.046981041, "training_stddev": 0.05040223328, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last average value is 5.052. The average for this metric is 5.047.", "is_anomalous": false}, {"value": 4.980695728, "average": 5.044431606, "min_value": 4.891232917, "max_value": 5.197630295, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "ba150f523a533eaa43b41ace1460d0d3", "metric_id": "3b560fa2fba18cb71f99a11812accb34", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "average", "anomaly_score": -1.248102282, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 4.980695728, "min_metric_value": 4.891232917, "max_metric_value": 5.197630295, "training_avg": 5.044431606, "training_stddev": 0.05106622974, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last average value is 4.981. The average for this metric is 5.044.", "is_anomalous": false}, {"value": 4.962525408, "average": 5.041398043, "min_value": 4.883907216, "max_value": 5.198888871, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "60c8ebbb6210159b2fc216a7a9f3e9cf", "metric_id": "e9a2802b05bc5289d802c7558920fef0", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "average", "anomaly_score": -1.502423416, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 4.962525408, "min_metric_value": 4.883907216, "max_metric_value": 5.198888871, "training_avg": 5.041398043, "training_stddev": 0.05249694249, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last average value is 4.963. The average for this metric is 5.041.", "is_anomalous": false}, {"value": 5.076461562, "average": 5.042650312, "min_value": 4.886830229, "max_value": 5.198470395, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "d6db3eab5a6cdaa52e9c6ce8d13adf9f", "metric_id": "c486f3abe4116881ea4567f17f9721a8", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "average", "anomaly_score": 0.6509671277, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 5.076461562, "min_metric_value": 4.886830229, "max_metric_value": 5.198470395, "training_avg": 5.042650312, "training_stddev": 0.05194002763, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last average value is 5.076. The average for this metric is 5.043.", "is_anomalous": false}, {"value": 5.076485931, "average": 5.043817057, "min_value": 4.889648134, "max_value": 5.197985981, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "a6d454649fc5f11dc1ee723ed361eaf8", "metric_id": "666917c428a49ed326429122a76b9485", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "average", "anomaly_score": 0.6357093181, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 5.076485931, "min_metric_value": 4.889648134, "max_metric_value": 5.197985981, "training_avg": 5.043817057, "training_stddev": 0.05138964121, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last average value is 5.076. The average for this metric is 5.044.", "is_anomalous": false}, {"value": 5.021562947, "average": 5.043075254, "min_value": 4.891098142, "max_value": 5.195052365, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "281046f102efc325f336dc0ee630c55b", "metric_id": "d36106fa82da1648a5abe2f33068db3d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "average", "anomaly_score": -0.4246489443, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 5.021562947, "min_metric_value": 4.891098142, "max_metric_value": 5.195052365, "training_avg": 5.043075254, "training_stddev": 0.05065903722, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last average value is 5.022. The average for this metric is 5.043.", "is_anomalous": false}], "result_description": "In column NULL_PERCENT_FLOAT, the last average value is 5.022. The average for this metric is 5.043."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "variance", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('variance' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_FLOAT, the last variance value is 5.476. The average for this metric is 4.96.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('variance' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Variance", "metrics": [{"value": 5.074005559, "average": 5.057372325, "min_value": 4.986803492, "max_value": 5.127941159, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "090fbec611788b032241ccf869b92f0f", "metric_id": "e8962134a5bb3234bf5094f6ef6d0172", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "variance", "anomaly_score": 0.7071067812, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 5.074005559, "min_metric_value": 4.986803492, "max_metric_value": 5.127941159, "training_avg": 5.057372325, "training_stddev": 0.02352294449, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last variance value is 5.074. The average for this metric is 5.057.", "is_anomalous": false}, {"value": 4.694924766, "average": 4.936556472, "min_value": 4.306798837, "max_value": 5.566314108, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "9db60a4eb2a9a69127e379f21e78ec44", "metric_id": "1dacb938d65ee9b4d7a548164c2d6918", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "variance", "anomaly_score": -1.151069995, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 4.694924766, "min_metric_value": 4.306798837, "max_metric_value": 5.566314108, "training_avg": 4.936556472, "training_stddev": 0.2099192119, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last variance value is 4.695. The average for this metric is 4.937.", "is_anomalous": false}, {"value": 5.015083382, "average": 4.9561882, "min_value": 4.428674197, "max_value": 5.483702202, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "e4798053b0f4cf52d02c101cc722ecc0", "metric_id": "9550b5815a1c1a0728723c42fe6d0c09", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "variance", "anomaly_score": 0.3349400126, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 5.015083382, "min_metric_value": 4.428674197, "max_metric_value": 5.483702202, "training_avg": 4.9561882, "training_stddev": 0.1758380009, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last variance value is 5.015. The average for this metric is 4.956.", "is_anomalous": false}, {"value": 5.053847936, "average": 4.975720147, "min_value": 4.500461659, "max_value": 5.450978635, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "622fcab0396f0ea90889450af94ce4b0", "metric_id": "b146a2aeffc9d3afb68582595e22cefd", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "variance", "anomaly_score": 0.4931702904, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 5.053847936, "min_metric_value": 4.500461659, "max_metric_value": 5.450978635, "training_avg": 4.975720147, "training_stddev": 0.158419496, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last variance value is 5.054. The average for this metric is 4.976.", "is_anomalous": false}, {"value": 5.038333708, "average": 4.986155741, "min_value": 4.55420992, "max_value": 5.418101561, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "469bd12edd2e2645a5d857dea723d4f1", "metric_id": "bc1fc68856efddc2183672b534f6d2e8", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "variance", "anomaly_score": 0.3623924495, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 5.038333708, "min_metric_value": 4.55420992, "max_metric_value": 5.418101561, "training_avg": 4.986155741, "training_stddev": 0.1439819401, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last variance value is 5.038. The average for this metric is 4.986.", "is_anomalous": false}, {"value": 4.798310444, "average": 4.959320698, "min_value": 4.511159431, "max_value": 5.407481965, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "7d3d37afda0579f58d74b66a621859d9", "metric_id": "30ba386e6346f9a463f0d11767f5fca4", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "variance", "anomaly_score": -1.077805686, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 4.798310444, "min_metric_value": 4.511159431, "max_metric_value": 5.407481965, "training_avg": 4.959320698, "training_stddev": 0.149387089, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last variance value is 4.798. The average for this metric is 4.959.", "is_anomalous": false}, {"value": 4.982807549, "average": 4.962256555, "min_value": 4.546592674, "max_value": 5.377920435, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "825e66ba1ba31336737769ee94e831bb", "metric_id": "c0f40b15b9da38f17a268d53c55fdfc6", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "variance", "anomaly_score": 0.1483241292, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 4.982807549, "min_metric_value": 4.546592674, "max_metric_value": 5.377920435, "training_avg": 4.962256555, "training_stddev": 0.1385546268, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last variance value is 4.983. The average for this metric is 4.962.", "is_anomalous": false}, {"value": 4.896612603, "average": 4.954962782, "min_value": 4.56064244, "max_value": 5.349283124, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "15d8f27cc45c9ad4a46850bb3f6df189", "metric_id": "73520a99b15d10be264422f5ead780c0", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "variance", "anomaly_score": -0.4439297649, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 4.896612603, "min_metric_value": 4.56064244, "max_metric_value": 5.349283124, "training_avg": 4.954962782, "training_stddev": 0.131440114, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last variance value is 4.897. The average for this metric is 4.955.", "is_anomalous": false}, {"value": 4.89805733, "average": 4.949272237, "min_value": 4.57360425, "max_value": 5.324940223, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "df338c21aeb5225958f6e6377bd0a225", "metric_id": "a8479e50da8052297963066c4d54a482", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "variance", "anomaly_score": -0.4089907231, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 4.89805733, "min_metric_value": 4.57360425, "max_metric_value": 5.324940223, "training_avg": 4.949272237, "training_stddev": 0.1252226622, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last variance value is 4.898. The average for this metric is 4.949.", "is_anomalous": false}, {"value": 5.064835043, "average": 4.959777947, "min_value": 4.58837464, "max_value": 5.331181253, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "bf93c44563d897e1e0f68f40911d3cb9", "metric_id": "c1651a3d21f69e9844cb8c37583e35e1", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "variance", "anomaly_score": 0.84859581, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 5.064835043, "min_metric_value": 4.58837464, "max_metric_value": 5.331181253, "training_avg": 4.959777947, "training_stddev": 0.1238011022, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last variance value is 5.065. The average for this metric is 4.96.", "is_anomalous": false}, {"value": 5.028067453, "average": 4.965468739, "min_value": 4.606445103, "max_value": 5.324492374, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "503c02141439abf66f0ba1031db5755b", "metric_id": "6e7f564ca4e6ed7964d33dc2f0458b03", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "variance", "anomaly_score": 0.5230745928, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 5.028067453, "min_metric_value": 4.606445103, "max_metric_value": 5.324492374, "training_avg": 4.965468739, "training_stddev": 0.1196745451, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last variance value is 5.028. The average for this metric is 4.965.", "is_anomalous": false}, {"value": 5.077201858, "average": 4.974063594, "min_value": 4.6179745, "max_value": 5.330152688, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "ae705b43465dde5dc23c8bee2ee82e74", "metric_id": "011f0d5b327d3f3c75415e0e53fa30eb", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "variance", "anomaly_score": 0.8689252121, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 5.077201858, "min_metric_value": 4.6179745, "max_metric_value": 5.330152688, "training_avg": 4.974063594, "training_stddev": 0.1186963648, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last variance value is 5.077. The average for this metric is 4.974.", "is_anomalous": false}, {"value": 4.967918298, "average": 4.973624644, "min_value": 4.63146983, "max_value": 5.315779459, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "c73c49b75e60ca358d09ca9a7d565384", "metric_id": "bce8ace7f56e4ffffea0de02836e44da", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "variance", "anomaly_score": -0.05003301716, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 4.967918298, "min_metric_value": 4.63146983, "max_metric_value": 5.315779459, "training_avg": 4.973624644, "training_stddev": 0.1140516048, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last variance value is 4.968. The average for this metric is 4.974.", "is_anomalous": false}, {"value": 4.771669367, "average": 4.960160959, "min_value": 4.595223512, "max_value": 5.325098407, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "8a718694b5ce4bfd3d37fc3fa35854fb", "metric_id": "11a2a56efb156606234069434fb712ff", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "variance", "anomaly_score": -1.549511514, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 4.771669367, "min_metric_value": 4.595223512, "max_metric_value": 5.325098407, "training_avg": 4.960160959, "training_stddev": 0.1216458158, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last variance value is 4.772. The average for this metric is 4.96.", "is_anomalous": false}, {"value": 4.827943524, "average": 4.95189737, "min_value": 4.585654235, "max_value": 5.318140504, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "e36988e43ee82cc4cc3a96b97857432d", "metric_id": "a7d158d8a4a5f6b28a96ac71b5a4585d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "variance", "anomaly_score": -1.015340633, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 4.827943524, "min_metric_value": 4.585654235, "max_metric_value": 5.318140504, "training_avg": 4.95189737, "training_stddev": 0.1220810449, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last variance value is 4.828. The average for this metric is 4.952.", "is_anomalous": false}, {"value": 5.211082685, "average": 4.967143565, "min_value": 4.565503221, "max_value": 5.368783908, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "75b68858cf831d7ec4a5e7eaf2e809d5", "metric_id": "540122a21a434dde1979549242fcc010", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "variance", "anomaly_score": 1.822071345, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 5.211082685, "min_metric_value": 4.565503221, "max_metric_value": 5.368783908, "training_avg": 4.967143565, "training_stddev": 0.1338801144, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last variance value is 5.211. The average for this metric is 4.967.", "is_anomalous": false}, {"value": 5.078672083, "average": 4.973339593, "min_value": 4.575790659, "max_value": 5.370888527, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "454710b6acf0a2f3bcf10e55f40304a8", "metric_id": "ebbafa5ab9b76065a52153a8e36e8758", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "variance", "anomaly_score": 0.7948643372, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 5.078672083, "min_metric_value": 4.575790659, "max_metric_value": 5.370888527, "training_avg": 4.973339593, "training_stddev": 0.1325163113, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last variance value is 5.079. The average for this metric is 4.973.", "is_anomalous": false}, {"value": 4.723343967, "average": 4.960181929, "min_value": 4.537252743, "max_value": 5.383111114, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "094ddc803a92eed8eefa48750f218ed1", "metric_id": "68d9d8eb0134807b4aeba50063b6a341", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "variance", "anomaly_score": -1.679983105, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 4.723343967, "min_metric_value": 4.537252743, "max_metric_value": 5.383111114, "training_avg": 4.960181929, "training_stddev": 0.1409763951, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last variance value is 4.723. The average for this metric is 4.96.", "is_anomalous": false}, {"value": 4.970202454, "average": 4.960682955, "min_value": 4.548979035, "max_value": 5.372386875, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "2d7e3eac7fb9c97000ea40b8f0104f22", "metric_id": "e50ec258154863c8289d41c196ab29d3", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "variance", "anomaly_score": 0.06936659224, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 4.970202454, "min_metric_value": 4.548979035, "max_metric_value": 5.372386875, "training_avg": 4.960682955, "training_stddev": 0.13723464, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last variance value is 4.97. The average for this metric is 4.961.", "is_anomalous": false}, {"value": 4.932529688, "average": 4.959342323, "min_value": 4.557639945, "max_value": 5.361044701, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "d35756b0b90a269606b1122c79bff2c9", "metric_id": "a5a160bdfce82e0cad861dcee0fa9e23", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "variance", "anomaly_score": -0.2002425459, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 4.932529688, "min_metric_value": 4.557639945, "max_metric_value": 5.361044701, "training_avg": 4.959342323, "training_stddev": 0.1339007926, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last variance value is 4.933. The average for this metric is 4.959.", "is_anomalous": false}, {"value": 4.88291457, "average": 4.955868335, "min_value": 4.56081094, "max_value": 5.350925729, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "88b968515fa561a98f2029a3e26f2dea", "metric_id": "b5035adcacad0527e5c4bc620111d26d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "variance", "anomaly_score": -0.553998725, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 4.88291457, "min_metric_value": 4.56081094, "max_metric_value": 5.350925729, "training_avg": 4.955868335, "training_stddev": 0.1316857981, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last variance value is 4.883. The average for this metric is 4.956.", "is_anomalous": false}, {"value": 4.88938085, "average": 4.952977574, "min_value": 4.564768826, "max_value": 5.341186323, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "a34d0f9de4be39aa6f28a1b5c90a59d8", "metric_id": "05757eab91960f26799b616f2c4f1d98", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "variance", "anomaly_score": -0.4914628341, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 4.88938085, "min_metric_value": 4.564768826, "max_metric_value": 5.341186323, "training_avg": 4.952977574, "training_stddev": 0.1294029162, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last variance value is 4.889. The average for this metric is 4.953.", "is_anomalous": false}, {"value": 4.917599104, "average": 4.951503471, "min_value": 4.571210216, "max_value": 5.331796726, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "1c9a7b4847d0fbb43cb4384f95f7f4ad", "metric_id": "588e81df3715123d139fa3948d85aac4", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "variance", "anomaly_score": -0.267459656, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 4.917599104, "min_metric_value": 4.571210216, "max_metric_value": 5.331796726, "training_avg": 4.951503471, "training_stddev": 0.1267644183, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last variance value is 4.918. The average for this metric is 4.952.", "is_anomalous": false}, {"value": 4.845290701, "average": 4.947254961, "min_value": 4.569553732, "max_value": 5.324956189, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "0aed0eb8a4fb1107d087e6c57a2ec4ae", "metric_id": "a1d02570be3ded867c16d19d2ddb5153", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "variance", "anomaly_score": -0.8098802867, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 4.845290701, "min_metric_value": 4.569553732, "max_metric_value": 5.324956189, "training_avg": 4.947254961, "training_stddev": 0.1259004095, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last variance value is 4.845. The average for this metric is 4.947.", "is_anomalous": false}, {"value": 4.974924549, "average": 4.948319176, "min_value": 4.57789117, "max_value": 5.318747181, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "b724eebe93dda714035293daf4febee4", "metric_id": "3362f464670d39a143cbb2b2a012585f", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "variance", "anomaly_score": 0.2154699914, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 4.974924549, "min_metric_value": 4.57789117, "max_metric_value": 5.318747181, "training_avg": 4.948319176, "training_stddev": 0.1234760017, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last variance value is 4.975. The average for this metric is 4.948.", "is_anomalous": false}, {"value": 5.085711039, "average": 4.953407763, "min_value": 4.581612782, "max_value": 5.325202745, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "4002ce6fd6e33ec1121902018254bf82", "metric_id": "7f9004ab7551c34fc06ee02dfe7c6e34", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "variance", "anomaly_score": 1.067550259, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 5.085711039, "min_metric_value": 4.581612782, "max_metric_value": 5.325202745, "training_avg": 4.953407763, "training_stddev": 0.1239316605, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last variance value is 5.086. The average for this metric is 4.953.", "is_anomalous": false}, {"value": 4.737715056, "average": 4.945704452, "min_value": 4.56091132, "max_value": 5.330497584, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "1e60f87dd56de75d2f2ff09ab97ba45b", "metric_id": "76117a374f911b7deccbbff875a4fcdb", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "variance", "anomaly_score": -1.621567889, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 4.737715056, "min_metric_value": 4.56091132, "max_metric_value": 5.330497584, "training_avg": 4.945704452, "training_stddev": 0.1282643775, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last variance value is 4.738. The average for this metric is 4.946.", "is_anomalous": false}, {"value": 4.849479432, "average": 4.942386348, "min_value": 4.560743513, "max_value": 5.324029183, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "63322e2ffe6a4d28219cecdaf3ba7e5d", "metric_id": "d52e5f4378b6ed8ac185773662fef1e8", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "variance", "anomaly_score": -0.7303183035, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 4.849479432, "min_metric_value": 4.560743513, "max_metric_value": 5.324029183, "training_avg": 4.942386348, "training_stddev": 0.1272142785, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last variance value is 4.849. The average for this metric is 4.942.", "is_anomalous": false}, {"value": 5.476192576, "average": 4.960179889, "min_value": 4.560743513, "max_value": 5.324029183, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "244c8674b1c9aa27639a9c664f36d6f8", "metric_id": "79d8c7dbf980755ad86925c5c1cb3995", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "variance", "anomaly_score": 3.255504991, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 5.476192576, "min_metric_value": 4.484665932, "max_metric_value": 5.435693846, "training_avg": 4.960179889, "training_stddev": 0.1585046524, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last variance value is 5.476. The average for this metric is 4.96.", "is_anomalous": true}], "result_description": "In column NULL_COUNT_FLOAT, the last variance value is 5.476. The average for this metric is 4.96."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_FLOAT, the last null_percent value is 80. The average for this metric is 5.567.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Null Percent", "metrics": [{"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "85682836dae5322c6226cadca1fb8742", "metric_id": "69437b7fa6dad9ed87c2a33f4179cf5e", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "5b1eb4febaa2ec5a836a596e4849a81d", "metric_id": "3215d757b1345cef10b4b0634a399dda", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "3106b267e3056488e36631e602e862df", "metric_id": "7fa4e346a0c3aa993aee89e973c811db", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "38410e6ccc6eaa33b3d4184e818e2950", "metric_id": "6d83078513e53d3b879fa744bcf7e010", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "1cf497e5d149c2322818bcea3a912e7c", "metric_id": "47abe57bab8862dd737a4418e68ad7aa", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "8656a3196c53a772ec107f1a46e5b4d3", "metric_id": "b77957e86a3807f6e4bd68acd919b730", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "04e1459a604467d0d99ae839f254a153", "metric_id": "75f4068e41d1111ad5318fa8f3e3a35b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "2cd60c65caf81f0fbd47488328cea121", "metric_id": "8c8d9526ba88a66a895e7cd6635e8fcb", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "24878fdcc75cb4347025f64ec0359926", "metric_id": "6e200be567b4a6ecf4f5edd03cda4a49", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "d4e82cb6dc9f2835da2e3fd6603c0632", "metric_id": "f89e5005d84ca1d3178538a651a9866d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "5e3f3c8637b8a50f61fe226c3bcba766", "metric_id": "5c2b33126a65851c6f5490cbbe29ca1b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "6117e955b1b5010f1574f1c5c6feb791", "metric_id": "0330ba1a677b5816901301460eef0d97", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "0f58a5054fca52f8dfc89fe074202382", "metric_id": "d31a8b44587c6ad5691fce3b88841eb1", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "c6c1680e5c0e9bc4b49c74f2b083edd2", "metric_id": "7d6d2ac3f02b055c714f1320e7c58589", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "377a0bd5b4a232cbd6de849bf9238fa1", "metric_id": "279015986e565f73bdcbc855ee23235f", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "394b27e87bc691918c22dc82451cf3be", "metric_id": "055a8b01482c274dc31197dd56d56b9e", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "2a82225e738349117db2688319ce286e", "metric_id": "c40c03c05858d9626edf9df1b9c2d216", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "eabfdf8cf1292b76c46845e9e27d20c9", "metric_id": "e3d2ea709be906c27c329f2bf36e0be6", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "8365ebf6c5413e3baf7e7fc46362fb43", "metric_id": "e58781d12b983df34823a8124f58432f", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "b86724d2cd731e7a4e75aebc584cc39c", "metric_id": "200e261c34863357a217195004a4258f", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "347abd705f5d44fd154925ac94c09a26", "metric_id": "04217a2c9c72249aa9d0ec57d62ce471", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "7534976c4ecfa1a7b317ebfe67488eb3", "metric_id": "8f19938f6742d0882dc92c5342e9cd46", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "86cc8fefa9e93722ca3151897e91f87c", "metric_id": "e6401ac3a890bad570a884e838deef72", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "3e780be9c3057a7bd811363bb14412c3", "metric_id": "89c92c6fb53e7b1ada2d19efb29ea414", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "d0a364d039b564a279d5362cedcf76a4", "metric_id": "f02ce025b0f6cd25350b101d09af3285", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "db9c775792839f0a904050fe1c50a6ae", "metric_id": "03bb7a3ca77f0ac363a745388f0a2d51", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "a1db93371d05d7fea397cb61b8039ac6", "metric_id": "7ee717b5c376372a70423c2942f4c843", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "fe7dad0bd5ab208eb955be13dd91a038", "metric_id": "09cca691786d496621cb4e82053fa083", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 80.0, "average": 5.566666667, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "f67667f4d3da1e9de9c9900c4ce5c617", "metric_id": "c8a636b6782bfbfe0c1619897c684baa", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "null_percent", "anomaly_score": 5.294651389, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 80.0, "min_metric_value": -36.607970261, "max_metric_value": 47.741303595, "training_avg": 5.566666667, "training_stddev": 14.058212309, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last null_percent value is 80. The average for this metric is 5.567.", "is_anomalous": true}], "result_description": "In column NULL_COUNT_FLOAT, the last null_percent value is 80. The average for this metric is 5.567."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "min_length", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('min_length' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_STR, the last min_length value is 5. The average for this metric is 5.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('min_length' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Min Length", "metrics": [{"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "d53c3732917c21295903c8829de99278", "metric_id": "eaf2ba69be93d5dbeeb5f3f2a09c4300", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "1334eff6c63ee2dc0ed1fa5462e57ba3", "metric_id": "06ca5902f31b353fc7c573c813535341", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "e05650be20642cd7ba62c54c77eb4214", "metric_id": "812adf54e9faf14a866bb9fd128e876d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "4ace5ff360924b4b88efc8ae70debf25", "metric_id": "70240e00d49ecdc5342ae73e24893b2d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "5a83dcfd938d32f32cf347bdbd194cbf", "metric_id": "b435b6ec4b76f304cec5f5bd8d5cb5df", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "d2a66b5f1f6665f659f40ab9d46d7dcf", "metric_id": "cf6c7fe37cab186cec85b82047a7d34f", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "89cdc8cea389982c163f886f3973a126", "metric_id": "9937eb214cf4147b56ea56638b27e718", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "ae0f1ec3730b163476f6d91c2138a6d3", "metric_id": "8e0e6be2012376e133cb93bcd27bdc97", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "ecd8bba41842506c0fee0cba9d1e74c5", "metric_id": "fb619276947959ccec00902a5579c1c6", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "8b57fa67efd28dacfd20653932b7d876", "metric_id": "0c10341303591a0faced755a1617e979", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "9288e3b19d08e23813cdf24f1f72cc4a", "metric_id": "b1b96895a7412e227ab0cdd45da622aa", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "839f062e671c641f6c981b55078a1e5c", "metric_id": "3e2808979ff0d1d1f78df8bfe9a88bdb", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "2d485273dabd3f79c886c2146ba17bb5", "metric_id": "1c11bf19cd1aaee74c422374a8dcd2af", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "7752cffccd62bf89a215019f7844f219", "metric_id": "c14cfbb3f73ec5c1812e1a6b0610aab0", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "e9836aa39745e03051585292923a774f", "metric_id": "2ad95a3d533e8afbb502b0992da7c2de", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "bf7773ff3e2b680514bef4a24741d406", "metric_id": "bdb869250fe05bbbff1ff8ee5b5da497", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "fac70ef7961fbb670be67fa590e229c6", "metric_id": "8d1a3a1270d4c835e6c7fe221555d7f6", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "33c4676ebdced7f98ca0ed9b15d064f4", "metric_id": "a21251b420a4ab222190e32394043515", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "637e334b8f4569174272f95f56c33545", "metric_id": "0e447a50f4c30b77a74c51ab2943898e", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "6fb3c67a1004d986f9b8d4f317e36aac", "metric_id": "6b6267f20d61fbd89cd2cbc0109aa44b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "0821b8e822a43e62547df7c309bd1a29", "metric_id": "2fc6665c6779d5875d81ab525469912b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "34e3ade896f2be5ae3bfcc145e7b90d0", "metric_id": "19bd89d8e7f4539338ea79ac08822ce3", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "0034dbc52debbc216b58df591a94d95f", "metric_id": "02186d08cad0ce6b170f183df6104abe", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "854cef63340c9c972494801a0080f6b7", "metric_id": "00348e283bd33a4ba1bd45e6f0b523d9", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "bf5cdb9d62e8f58912a9a45e7342e549", "metric_id": "c00f8250c3cade74971994f38850bdd7", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "2bb2eed4c4896a7475b0d4e174bbc482", "metric_id": "a60ea29c22d80488148c50629a828018", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "afaa482bda1e846919eb6e3d1f436c80", "metric_id": "e10d7c3e4ed9d3bbd94ca862fc899761", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "c7ad503949c749bda7a76a32f7363219", "metric_id": "d1b7289f15c7f68776063f63fee3966b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "1a7b15b7c941eaa55d42bda05b39bfc3", "metric_id": "a5ffa0962438dd4635989d10d8e86d4c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}], "result_description": "In column NULL_PERCENT_STR, the last min_length value is 5. The average for this metric is 5."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "average", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_INT, the last average value is 150.948. The average for this metric is 150.016.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Average", "metrics": [{"value": 151.385093168, "average": 150.526575811, "min_value": 146.884195145, "max_value": 154.168956478, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "9d73a111c1e04f8aa6dafa74a5affa7b", "metric_id": "cc488744194f071c5c12806427f60cd5", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "average", "anomaly_score": 0.7071067812, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 151.385093168, "min_metric_value": 146.884195145, "max_metric_value": 154.168956478, "training_avg": 150.526575811, "training_stddev": 1.214126889, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last average value is 151.385. The average for this metric is 150.527.", "is_anomalous": false}, {"value": 150.528050491, "average": 150.527067371, "min_value": 147.951514036, "max_value": 153.102620707, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "a07c6823904f0f408f26fc734eef8ea7", "metric_id": "b8640d4edff30553c5c64f98812fba2d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "average", "anomaly_score": 0.001145136041, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 150.528050491, "min_metric_value": 147.951514036, "max_metric_value": 153.102620707, "training_avg": 150.527067371, "training_stddev": 0.8585177785, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last average value is 150.528. The average for this metric is 150.527.", "is_anomalous": false}, {"value": 148.225174825, "average": 149.951594235, "min_value": 145.908771883, "max_value": 153.994416586, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "2948930e19a7042269230cf119c3a613", "metric_id": "5d693eee46ab519f031af3fe688b51b1", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "average", "anomaly_score": -1.281099632, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 148.225174825, "min_metric_value": 145.908771883, "max_metric_value": 153.994416586, "training_avg": 149.951594235, "training_stddev": 1.347607451, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last average value is 148.225. The average for this metric is 149.952.", "is_anomalous": false}, {"value": 149.776223776, "average": 149.916520143, "min_value": 146.407436493, "max_value": 153.425603793, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "901db7f945777569403d6e205010bf21", "metric_id": "56493a6cc894b85c07c8faf90846591e", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "average", "anomaly_score": -0.1199427379, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 149.776223776, "min_metric_value": 146.407436493, "max_metric_value": 153.425603793, "training_avg": 149.916520143, "training_stddev": 1.16969455, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last average value is 149.776. The average for this metric is 149.917.", "is_anomalous": false}, {"value": 150.478902954, "average": 150.010250611, "min_value": 146.796942956, "max_value": 153.223558267, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "e26a46ed3170466984bb51ad60e15156", "metric_id": "d379ade18d49031dd97934a50fa8b1e1", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "average", "anomaly_score": 0.4375419902, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 150.478902954, "min_metric_value": 146.796942956, "max_metric_value": 153.223558267, "training_avg": 150.010250611, "training_stddev": 1.071102552, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last average value is 150.479. The average for this metric is 150.01.", "is_anomalous": false}, {"value": 150.514705882, "average": 150.08231565, "min_value": 147.093731141, "max_value": 153.07090016, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "e1d7d9389e379b4ed923f6d284d11f8c", "metric_id": "15c6cc8892ae7476557d771cb3fc8d76", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "average", "anomaly_score": 0.4340418324, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 150.514705882, "min_metric_value": 147.093731141, "max_metric_value": 153.07090016, "training_avg": 150.08231565, "training_stddev": 0.9961948365, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last average value is 150.515. The average for this metric is 150.082.", "is_anomalous": false}, {"value": 148.692307692, "average": 149.908564655, "min_value": 146.773388973, "max_value": 153.043740338, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "3f8da89b15563a67dc908be478342034", "metric_id": "821885e7d51cdeebd3223e557d882d56", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "average", "anomaly_score": -1.163817042, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 148.692307692, "min_metric_value": 146.773388973, "max_metric_value": 153.043740338, "training_avg": 149.908564655, "training_stddev": 1.045058561, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last average value is 148.692. The average for this metric is 149.909.", "is_anomalous": false}, {"value": 150.454992968, "average": 149.969278912, "min_value": 146.986118735, "max_value": 152.95243909, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "a09d6f9d6b54934009f018b6be579de4", "metric_id": "7bd501a616cb20ff708ebd350d5764ae", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "average", "anomaly_score": 0.4884558922, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 150.454992968, "min_metric_value": 146.986118735, "max_metric_value": 152.95243909, "training_avg": 149.969278912, "training_stddev": 0.9943867257, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last average value is 150.455. The average for this metric is 149.969.", "is_anomalous": false}, {"value": 150.30474198, "average": 150.002825219, "min_value": 147.172326746, "max_value": 152.833323692, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "f9f0ec58554eb61595889121d0965672", "metric_id": "6926f0313ec0c311e312066a96438462", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "average", "anomaly_score": 0.31999674, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 150.30474198, "min_metric_value": 147.172326746, "max_metric_value": 152.833323692, "training_avg": 150.002825219, "training_stddev": 0.943499491, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last average value is 150.305. The average for this metric is 150.003.", "is_anomalous": false}, {"value": 149.894479385, "average": 149.992975598, "min_value": 147.305941185, "max_value": 152.68001001, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "1d49e93b379148a2ff231dc8220d522b", "metric_id": "8468f006175f26190a21186ed24eff24", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "average", "anomaly_score": -0.1099683119, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 149.894479385, "min_metric_value": 147.305941185, "max_metric_value": 152.68001001, "training_avg": 149.992975598, "training_stddev": 0.8956781375, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last average value is 149.894. The average for this metric is 149.993.", "is_anomalous": false}, {"value": 150.302821748, "average": 150.01879611, "min_value": 147.442795376, "max_value": 152.594796844, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "32661740793e42b04600ac95413b128f", "metric_id": "5ea759921a3e42630c3974129f368931", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "average", "anomaly_score": 0.330775105, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 150.302821748, "min_metric_value": 147.442795376, "max_metric_value": 152.594796844, "training_avg": 150.01879611, "training_stddev": 0.8586669113, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last average value is 150.303. The average for this metric is 150.019.", "is_anomalous": false}, {"value": 150.482734807, "average": 150.054483702, "min_value": 147.558124388, "max_value": 152.550843017, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "3daa82c57d3ef39e6da4d23e2511597d", "metric_id": "96f7461ecd09523e39537a3c71048f78", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "average", "anomaly_score": 0.5146507978, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 150.482734807, "min_metric_value": 147.558124388, "max_metric_value": 152.550843017, "training_avg": 150.054483702, "training_stddev": 0.8321197715, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last average value is 150.483. The average for this metric is 150.054.", "is_anomalous": false}, {"value": 149.257753274, "average": 149.997574386, "min_value": 147.515536454, "max_value": 152.479612318, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "909523690f444471556a944ce7454a08", "metric_id": "4ae355130cb321b0e2ace50af033b102", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "average", "anomaly_score": -0.8942100798, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 149.257753274, "min_metric_value": 147.515536454, "max_metric_value": 152.479612318, "training_avg": 149.997574386, "training_stddev": 0.8273459774, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last average value is 149.258. The average for this metric is 149.998.", "is_anomalous": false}, {"value": 148.679258242, "average": 149.909686643, "min_value": 147.309061684, "max_value": 152.510311602, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "6ca442a11dc7d5396579c1c24916fdc3", "metric_id": "2cd726ce3596e5cc037d6139be03dfeb", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "average", "anomaly_score": -1.419383903, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 148.679258242, "min_metric_value": 147.309061684, "max_metric_value": 152.510311602, "training_avg": 149.909686643, "training_stddev": 0.8668749863, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last average value is 148.679. The average for this metric is 149.91.", "is_anomalous": false}, {"value": 149.753613214, "average": 149.899932054, "min_value": 147.384764321, "max_value": 152.415099786, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "95cd7967921b2e1edea58bf2aff54175", "metric_id": "a2c80f23274c2e18e1a81895860ad99d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "average", "anomaly_score": -0.1745237559, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 149.753613214, "min_metric_value": 147.384764321, "max_metric_value": 152.415099786, "training_avg": 149.899932054, "training_stddev": 0.8383892441, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last average value is 149.754. The average for this metric is 149.9.", "is_anomalous": false}, {"value": 149.980582524, "average": 149.904676199, "min_value": 147.468668608, "max_value": 152.34068379, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "4871120245448d2b04fe078fd3a3e314", "metric_id": "8e4bbed52010d0145c8335663bccf814", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "average", "anomaly_score": 0.09348040469, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 149.980582524, "min_metric_value": 147.468668608, "max_metric_value": 152.34068379, "training_avg": 149.904676199, "training_stddev": 0.8120025304, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last average value is 149.981. The average for this metric is 149.905.", "is_anomalous": false}, {"value": 150.345017182, "average": 149.929139587, "min_value": 147.54544153, "max_value": 152.312837645, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "72295a3ed528f849a0d3e66c56e18454", "metric_id": "c03bb1d432b6dd00715e34809e3439da", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "average", "anomaly_score": 0.5234021907, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 150.345017182, "min_metric_value": 147.54544153, "max_metric_value": 152.312837645, "training_avg": 149.929139587, "training_stddev": 0.7945660192, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last average value is 150.345. The average for this metric is 149.929.", "is_anomalous": false}, {"value": 149.374216028, "average": 149.899933084, "min_value": 147.552122351, "max_value": 152.247743817, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "cbee51319eae122a094e3a3ee550d4c4", "metric_id": "65c92dea72c1f4ae5a2cde5bc777653e", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "average", "anomaly_score": -0.6717539646, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 149.374216028, "min_metric_value": 147.552122351, "max_metric_value": 152.247743817, "training_avg": 149.899933084, "training_stddev": 0.7826035778, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last average value is 149.374. The average for this metric is 149.9.", "is_anomalous": false}, {"value": 149.656696125, "average": 149.887771236, "min_value": 147.596762169, "max_value": 152.178780303, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "fc14d3bfa2cd8b8edf90e159f36ca75c", "metric_id": "e9b38f7be658eaba219ffbacbcdb2bbc", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "average", "anomaly_score": -0.3025851546, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 149.656696125, "min_metric_value": 147.596762169, "max_metric_value": 152.178780303, "training_avg": 149.887771236, "training_stddev": 0.7636696891, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last average value is 149.657. The average for this metric is 149.888.", "is_anomalous": false}, {"value": 150.692145863, "average": 149.92607479, "min_value": 147.63182544, "max_value": 152.220324139, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "85476a6afc639c6f5addf7e6c2513388", "metric_id": "33902c5973f1a322ce2bb38593f0d9ea", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "average", "anomaly_score": 1.001727741, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 150.692145863, "min_metric_value": 147.63182544, "max_metric_value": 152.220324139, "training_avg": 149.92607479, "training_stddev": 0.7647497833, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last average value is 150.692. The average for this metric is 149.926.", "is_anomalous": false}, {"value": 151.307639367, "average": 149.98887318, "min_value": 147.581847279, "max_value": 152.39589908, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "e1b34eefa1a107c0efe0c31532f1e8b6", "metric_id": "241befcf65c1577419dbc188828b362b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "average", "anomaly_score": 1.643646028, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 151.307639367, "min_metric_value": 147.581847279, "max_metric_value": 152.39589908, "training_avg": 149.98887318, "training_stddev": 0.8023419669, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last average value is 151.308. The average for this metric is 149.989.", "is_anomalous": false}, {"value": 150.601648352, "average": 150.015515578, "min_value": 147.63279601, "max_value": 152.398235147, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "d18d8c6cb8b5fcddc71e721905ccffdc", "metric_id": "0960fa7073a6fc4f97fd933ba3a2ebd1", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "average", "anomaly_score": 0.7379795521, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 150.601648352, "min_metric_value": 147.63279601, "max_metric_value": 152.398235147, "training_avg": 150.015515578, "training_stddev": 0.7942398562, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last average value is 150.602. The average for this metric is 150.016.", "is_anomalous": false}, {"value": 149.919642857, "average": 150.011520882, "min_value": 147.680435733, "max_value": 152.34260603, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "13c76db68c70606fa465413776df8bad", "metric_id": "c75e9d462532f4826ecbdfbf92902740", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "average", "anomaly_score": -0.1182428165, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 149.919642857, "min_metric_value": 147.680435733, "max_metric_value": 152.34260603, "training_avg": 150.011520882, "training_stddev": 0.7770283828, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last average value is 149.92. The average for this metric is 150.012.", "is_anomalous": false}, {"value": 149.090529248, "average": 149.974681216, "min_value": 147.626723749, "max_value": 152.322638683, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "1cadde75cc878ad74e992efc6d3e783c", "metric_id": "1d2ee7e4c20d283554e4be07021f3a9c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "average", "anomaly_score": -1.129686522, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 149.090529248, "min_metric_value": 147.626723749, "max_metric_value": 152.322638683, "training_avg": 149.974681216, "training_stddev": 0.782652489, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last average value is 149.091. The average for this metric is 149.975.", "is_anomalous": false}, {"value": 149.564827586, "average": 149.958917615, "min_value": 147.645795262, "max_value": 152.272039968, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "d2a94a3a899491b6be4f24b15cd3946f", "metric_id": "5400df38e5d379a5162579f0a62f6f79", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "average", "anomaly_score": -0.5111143755, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 149.564827586, "min_metric_value": 147.645795262, "max_metric_value": 152.272039968, "training_avg": 149.958917615, "training_stddev": 0.7710407842, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last average value is 149.565. The average for this metric is 149.959.", "is_anomalous": false}, {"value": 150.545582048, "average": 149.980645927, "min_value": 147.68729243, "max_value": 152.273999424, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "4a9c5a44c19d0888343da28aa6a8d127", "metric_id": "c80a2d6e9c5592611af72f9c2b15a2d5", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "average", "anomaly_score": 0.7390087761, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 150.545582048, "min_metric_value": 147.68729243, "max_metric_value": 152.273999424, "training_avg": 149.980645927, "training_stddev": 0.7644511656, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last average value is 150.546. The average for this metric is 149.981.", "is_anomalous": false}, {"value": 149.125781793, "average": 149.950115065, "min_value": 147.648034981, "max_value": 152.25219515, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "587dfca516e27e2bc72fb450055ee126", "metric_id": "a551b43ec5d5b26b4d5a946017663cc3", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "average", "anomaly_score": -1.074245781, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 149.125781793, "min_metric_value": 147.648034981, "max_metric_value": 152.25219515, "training_avg": 149.950115065, "training_stddev": 0.7673600283, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last average value is 149.126. The average for this metric is 149.95.", "is_anomalous": false}, {"value": 150.93877551, "average": 149.984206805, "min_value": 147.657481928, "max_value": 152.310931682, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "5fbe524ee64d8bd83a5c48cb40926274", "metric_id": "c554cfc65b4c78f471d25063d9702950", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "average", "anomaly_score": 1.230788455, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 150.93877551, "min_metric_value": 147.657481928, "max_metric_value": 152.310931682, "training_avg": 149.984206805, "training_stddev": 0.7755749589, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last average value is 150.939. The average for this metric is 149.984.", "is_anomalous": false}, {"value": 150.947916667, "average": 150.016330467, "min_value": 147.669930646, "max_value": 152.362730288, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "b55c04eb1a2e43b37bc81121e893ec0a", "metric_id": "8ea4198b6e46cd722d04f70991d4021a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "average", "anomaly_score": 1.191083708, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 150.947916667, "min_metric_value": 147.669930646, "max_metric_value": 152.362730288, "training_avg": 150.016330467, "training_stddev": 0.7821332737, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last average value is 150.948. The average for this metric is 150.016.", "is_anomalous": false}], "result_description": "In column NULL_PERCENT_INT, the last average value is 150.948. The average for this metric is 150.016."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_BOOL", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_BOOL' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_BOOL, the last null_percent value is 80. The average for this metric is 5.567.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_BOOL' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Null Percent", "metrics": [{"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "ede4d0a6619faaa3472674fd8d2ac068", "metric_id": "0d0019fa2bb62aee320df63eb86821b3", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "7faf3b2676688bb7a216d80b2cc9e46a", "metric_id": "ebe5e88b5d5f8eb7df045150dc80f750", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "60cab88a9b00dd544491bd9d77f316db", "metric_id": "4863f68c6c80a2fd2de158ff6c1dba76", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "1c46ee99a474d3ad4eec17be804f764c", "metric_id": "71a9d5c91b4ce7e595a5f192e281ccf0", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "f7a58ba7dd8cf745712344cd7fe59efc", "metric_id": "c08fe69a32187e6b9eee211146b63e21", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "66c4b5837441ea75c793024640b78a09", "metric_id": "bc12e9ab5cf987392008f1c88fef6483", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "710030eb4a963765fd72dc592b11dec1", "metric_id": "f091f4cb6909ebe3ef7f9773d81f2f89", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "c80753744e1332dd18d04db0e9013fdf", "metric_id": "bcf592d28a32c020bf71fd22dc436f8e", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "cc9142b07381e11638cbcae0cad1d3a0", "metric_id": "c53d21ec8cb326c46c05a212e5861f40", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "716a19358714b3d6b927288f51f92150", "metric_id": "ef2e408bff8410025c6336f759617404", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "485c7e2f0ea3dad9c427d16ffd0aee14", "metric_id": "bfef6c7d8309eb32c889bc250e7dead8", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "5d347ca8088c1c299818d5d0355e3b9f", "metric_id": "02a76608e1eab66769e360772164b725", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "e74e8014afda609a4e155d97a15ad720", "metric_id": "17fbd05065832e7f42db9a598464b5da", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "9f89c12989472f06dfdf967315addf63", "metric_id": "a950b3f69c1cc68582cce46cea8eff85", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "307ca3e9a202720541f99acc4fc7d262", "metric_id": "240958b836f64f9b2b2ec05e5fbeea8c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "d22c47e805a05a2c162dce01a5d46a8a", "metric_id": "b592dda4457b6e6d5457e3bb351c56c2", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "4537b0ab50b616d0ae464415ebd2e0d7", "metric_id": "657b46722f247548015407815363f985", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "8e0abbb3711bf2eb6a43af10c9eba704", "metric_id": "9ac5f022977c6b028a5a60343e9f3319", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "d31ae30fa8dcd32c5e50728c42cc5586", "metric_id": "968fa1c0b64848c13b42d23a2fd23428", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "b841d9e17a121a9864af5e971bfb4c92", "metric_id": "c8d6ffb3681335b3975a794f7aa979bd", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "21a7f61ee28512a372aa8d120b4c1768", "metric_id": "0a2044b78c8664dc057bfac7c34048eb", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "a9af44d66de27e25f9e6b77e046d4582", "metric_id": "3f2ef75606fc27705657530c5f7126e6", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "72afd17af02740bf5f2f16b3b81b09c7", "metric_id": "9ed8e63675c348220fae1ee6089fd53c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "1ebfd135b919fce498a75aa77be947b6", "metric_id": "601e7054f57204869398b006d1961f14", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "306fb138092d7f653cfb24db78effa76", "metric_id": "4466099a850d3a6013ecfdd71de7f305", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "14b71487fb207b184aaed0339d5283fe", "metric_id": "5908fa90d3e680d13bd68ad188ed010b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "b4c9e5efe2c8d6c2c723de8489371413", "metric_id": "0d2c4bc030fd5a9adcb2d004ad765ee9", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "e76245901dfdcafaabdfa3e33e0bf352", "metric_id": "1454f239131f0e5656153759a818067a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 80.0, "average": 5.566666667, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "1f83ff62fc48aa5b99baae78fa3d9ae6", "metric_id": "27faf1899e2c4a524ea63ca3afb3b2df", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_percent", "anomaly_score": 5.294651389, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 80.0, "min_metric_value": -36.607970261, "max_metric_value": 47.741303595, "training_avg": 5.566666667, "training_stddev": 14.058212309, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_percent value is 80. The average for this metric is 5.567.", "is_anomalous": true}], "result_description": "In column NULL_COUNT_BOOL, the last null_percent value is 80. The average for this metric is 5.567."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_STR, the last null_percent value is 80. The average for this metric is 5.567.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Null Percent", "metrics": [{"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "5c7db6202d98f744a3c535bf44d616a6", "metric_id": "62198d59612280fe31c807fb1cffa5a7", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "f72e170f358ecc0c4228b7e23d92cd4a", "metric_id": "07ce05a6df2ec6eb04e9839656751ee9", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "0bf723de9e77c4c920a78fce809cf19a", "metric_id": "87fc3584c70cc06f3460e6e1ad31625f", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "4c9a4dce69cbbc81928ce4875cfe0fa9", "metric_id": "a8303183bc5131cfdd95c72a57564170", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "c33fbd4f6539590f10af62d736957fab", "metric_id": "083cee5f85dc9dd6964fa4c2353cddcc", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "9878d92653eafe575e6298d89fed7dd6", "metric_id": "78b6217d0ecb6cc2e20cd3e572d8ca56", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "d91c288497dac4182e6c0d5695d63cbd", "metric_id": "c0098ecccc589f5d14246b1240ebb1f6", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "dfe5a47c9e1d6ec3bfb047dbd34a8a79", "metric_id": "b63c7efe0f52689013ed90f1bce82f56", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "8f69802331ae154c8b0a8e965dff867e", "metric_id": "dc90a6e3850e96c33ae46e8ac3b75898", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "3698de99671cb5f05071dbcda8c9ac26", "metric_id": "fb2d0d7da757d70cf2dc8d95c9c070f3", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "3c003837c8b4346bbc3575ec4ea9a516", "metric_id": "4415ca3bc95517ab8254c093f009de20", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "e635461a0ad9ac95517d20be70d3ab4c", "metric_id": "ccfb5edd5b34577d7324dbb0e2c80304", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "c76ac37dfd96f0f152cc8eff1c4d62a9", "metric_id": "93f849bd66772a54dce339c442ec499b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "6998b08e5ced1b480ff4e9f2ceb5805e", "metric_id": "f64704b2a38d9e7b5efb51ceaf94cf47", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "e4a3bdcbf9b257a9923c835549132759", "metric_id": "8063dd2e859d4f6b6b43ae991f14c0b8", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "71310a56f9f2feea578739d53f3d64fc", "metric_id": "24b43dab38c5abaa94e56b00fad5fa30", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "8a4be6e832cfd71075b26850b8e857f2", "metric_id": "8716bd5832edcb469c5a47f0d3baf2d2", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "f09760e58c43ca4c7e219fa50eaebe27", "metric_id": "553f92abf96a53ca83dffe8f1ed7ebef", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "ff3d95e3c977e155b03f5915f08e3444", "metric_id": "b8cc82862858ee3465e58968a93d9ea2", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "c455a9ba1075769af875548cbb12a360", "metric_id": "6bfc9265f060dd7a70d571bd2076113d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "5c052a6936a50c92a3894404a30478c2", "metric_id": "ae7c04859ec92bb2089aedbc97aaa63e", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "1dfbdb4c131ddd7780abb807b7249329", "metric_id": "c7b4afb9b70c2b5ed4b63ec5d0fe1578", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "2a6f1d13a193053d30f62657a1ad1c83", "metric_id": "7a6f958aa3f22cdf1c9f97d0839f599e", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "98d95779b197080061261411b0b97903", "metric_id": "fb4512f01ddcd4a60646e464fd0fe17a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "a93bf06ecfc2fd89d7f9f66c8838135a", "metric_id": "3e5c3153733740ce5a595e23641a9082", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "4cdaa3cd2e189ddb56a6351f86fe9048", "metric_id": "6b094930cf03478748f4576dc63bea9a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "ad2d487be8d1b4d10b87ba94dbd480ce", "metric_id": "6a7ba4922faf471c7d12244a56992329", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "04ed195e734c70dffe7b93fa594c1ad0", "metric_id": "84db342ab4cb8f53e8a3384f124a8b91", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 80.0, "average": 5.566666667, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "fa31ae683410451672343f6112a89daa", "metric_id": "b7a7aa45dcd0e514a972046dc0230fde", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "null_percent", "anomaly_score": 5.294651389, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 80.0, "min_metric_value": -36.607970261, "max_metric_value": 47.741303595, "training_avg": 5.566666667, "training_stddev": 14.058212309, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last null_percent value is 80. The average for this metric is 5.567.", "is_anomalous": true}], "result_description": "In column NULL_COUNT_STR, the last null_percent value is 80. The average for this metric is 5.567."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "variance", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('variance' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_FLOAT, the last variance value is 5.206. The average for this metric is 4.956.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('variance' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Variance", "metrics": [{"value": 5.188079131, "average": 5.192593104, "min_value": 5.173441938, "max_value": 5.211744271, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "3d27a71058979317e5432fe96bcfbc66", "metric_id": "fd07cba29c24d57bceb75273f6591421", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "variance", "anomaly_score": -0.7071067811, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 5.188079131, "min_metric_value": 5.173441938, "max_metric_value": 5.211744271, "training_avg": 5.192593104, "training_stddev": 0.006383722306, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last variance value is 5.188. The average for this metric is 5.193.", "is_anomalous": false}, {"value": 4.864915931, "average": 5.08336738, "min_value": 4.515652334, "max_value": 5.651082426, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "3614e4c946d2a07ff94ee227346a2a50", "metric_id": "ec20e0c32760f19b94f3e9ac9feec24b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "variance", "anomaly_score": -1.154371989, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 4.864915931, "min_metric_value": 4.515652334, "max_metric_value": 5.651082426, "training_avg": 5.08336738, "training_stddev": 0.1892383488, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last variance value is 4.865. The average for this metric is 5.083.", "is_anomalous": false}, {"value": 5.087000233, "average": 5.084275593, "min_value": 4.62070617, "max_value": 5.547845017, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "79fe14931c5f6ce4ecca38ff7b50a996", "metric_id": "d4f1400839f352308dd93906072550f8", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "variance", "anomaly_score": 0.01763256633, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 5.087000233, "min_metric_value": 4.62070617, "max_metric_value": 5.547845017, "training_avg": 5.084275593, "training_stddev": 0.1545231412, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last variance value is 5.087. The average for this metric is 5.084.", "is_anomalous": false}, {"value": 5.179777401, "average": 5.103375955, "min_value": 4.681962228, "max_value": 5.524789682, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "4e686d77f42e7f3c286fd4e194fcd6c3", "metric_id": "22a1091bb7b589bf8574c6a86704fb2d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "variance", "anomaly_score": 0.5438938584, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 5.179777401, "min_metric_value": 4.681962228, "max_metric_value": 5.524789682, "training_avg": 5.103375955, "training_stddev": 0.1404712423, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last variance value is 5.18. The average for this metric is 5.103.", "is_anomalous": false}, {"value": 5.042533809, "average": 5.093235597, "min_value": 4.709016553, "max_value": 5.477454641, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "2c2735973e1af3458ccbc724691d1dbe", "metric_id": "0c42b9b445254d255010eb5d520d482e", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "variance", "anomaly_score": -0.3958818968, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 5.042533809, "min_metric_value": 4.709016553, "max_metric_value": 5.477454641, "training_avg": 5.093235597, "training_stddev": 0.1280730145, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last variance value is 5.043. The average for this metric is 5.093.", "is_anomalous": false}, {"value": 4.888963838, "average": 5.064053917, "min_value": 4.643733604, "max_value": 5.48437423, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "78bc79980dbf2dcacfe50b4bcc63bae1", "metric_id": "de357d4ece87f4804de669947441f4c2", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "variance", "anomaly_score": -1.249690348, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 4.888963838, "min_metric_value": 4.643733604, "max_metric_value": 5.48437423, "training_avg": 5.064053917, "training_stddev": 0.1401067711, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last variance value is 4.889. The average for this metric is 5.064.", "is_anomalous": false}, {"value": 4.978898085, "average": 5.053409438, "min_value": 4.653923933, "max_value": 5.452894943, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "f200d9c19e38c3fa0619584007d7350f", "metric_id": "1fc6536f30ce82dd881003a360aacce2", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "variance", "anomaly_score": -0.5595548681, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 4.978898085, "min_metric_value": 4.653923933, "max_metric_value": 5.452894943, "training_avg": 5.053409438, "training_stddev": 0.133161835, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last variance value is 4.979. The average for this metric is 5.053.", "is_anomalous": false}, {"value": 4.786071103, "average": 5.023705179, "min_value": 4.564238237, "max_value": 5.483172121, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "bfdbfb0dbbe7daea8de97866f5955cec", "metric_id": "859a3bcb2e8addb92114507a4c95621b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "variance", "anomaly_score": -1.551585463, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 4.786071103, "min_metric_value": 4.564238237, "max_metric_value": 5.483172121, "training_avg": 5.023705179, "training_stddev": 0.1531556473, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last variance value is 4.786. The average for this metric is 5.024.", "is_anomalous": false}, {"value": 4.932295807, "average": 5.014564241, "min_value": 4.572779991, "max_value": 5.456348492, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "48ab666fc0622bec535a0c74ae12cb84", "metric_id": "3557d5c6a0dd12fc10dbdc9b28701ede", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "variance", "anomaly_score": -0.5586557308, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 4.932295807, "min_metric_value": 4.572779991, "max_metric_value": 5.456348492, "training_avg": 5.014564241, "training_stddev": 0.1472614169, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last variance value is 4.932. The average for this metric is 5.015.", "is_anomalous": false}, {"value": 5.104366622, "average": 5.022728094, "min_value": 4.5958157, "max_value": 5.449640489, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "00665970fe1ae538922604ed0f293211", "metric_id": "9a3a2cc77df2c0d7c43a23ffd7140fc0", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "variance", "anomaly_score": 0.5736904926, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 5.104366622, "min_metric_value": 4.5958157, "max_metric_value": 5.449640489, "training_avg": 5.022728094, "training_stddev": 0.1423041315, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last variance value is 5.104. The average for this metric is 5.023.", "is_anomalous": false}, {"value": 4.810571247, "average": 5.005048357, "min_value": 4.558457352, "max_value": 5.451639362, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "c6aa34c26fa6802cab10e645c3f428a2", "metric_id": "de75d6a2389835f390f4f6769c1ec5d3", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "variance", "anomaly_score": -1.30641084, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 4.810571247, "min_metric_value": 4.558457352, "max_metric_value": 5.451639362, "training_avg": 5.005048357, "training_stddev": 0.1488636682, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last variance value is 4.811. The average for this metric is 5.005.", "is_anomalous": false}, {"value": 4.771699268, "average": 4.987098427, "min_value": 4.517502186, "max_value": 5.456694668, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "3040ff0c2bb02fb449f0b7cf6cc9fb4d", "metric_id": "8e72f2f2c54ad8700ead77b843c202ca", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "variance", "anomaly_score": -1.376070377, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 4.771699268, "min_metric_value": 4.517502186, "max_metric_value": 5.456694668, "training_avg": 4.987098427, "training_stddev": 0.1565320804, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last variance value is 4.772. The average for this metric is 4.987.", "is_anomalous": false}, {"value": 5.121239335, "average": 4.996679921, "min_value": 4.532864293, "max_value": 5.460495548, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "d9fa9d6843ed17e4b8a0ab70d98cbb39", "metric_id": "0c82a6a81f72a8961f55b282eae081a7", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "variance", "anomaly_score": 0.8056611769, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 5.121239335, "min_metric_value": 4.532864293, "max_metric_value": 5.460495548, "training_avg": 4.996679921, "training_stddev": 0.1546052092, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last variance value is 5.121. The average for this metric is 4.997.", "is_anomalous": false}, {"value": 5.086187311, "average": 5.00264708, "min_value": 4.550357556, "max_value": 5.454936604, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "bd9061d1bc2d7a685b04058ee8ec8bea", "metric_id": "f97a5e794b41eef469aeaa4d50c19e16", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "variance", "anomaly_score": 0.5541156302, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 5.086187311, "min_metric_value": 4.550357556, "max_metric_value": 5.454936604, "training_avg": 5.00264708, "training_stddev": 0.1507631746, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last variance value is 5.086. The average for this metric is 5.003.", "is_anomalous": false}, {"value": 5.060609361, "average": 5.006269722, "min_value": 4.567159392, "max_value": 5.445380053, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "b62ee73cb1343e004b641141f98db9b4", "metric_id": "0624d51dab57d4dc43e354e49f53c19c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "variance", "anomaly_score": 0.3712481916, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 5.060609361, "min_metric_value": 4.567159392, "max_metric_value": 5.445380053, "training_avg": 5.006269722, "training_stddev": 0.1463701101, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last variance value is 5.061. The average for this metric is 5.006.", "is_anomalous": false}, {"value": 4.748247601, "average": 4.991091951, "min_value": 4.526320424, "max_value": 5.455863478, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "74e4b730754756a38435bde231070873", "metric_id": "b91406c1665ba151298c8b3f94a858f1", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "variance", "anomaly_score": -1.567507916, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 4.748247601, "min_metric_value": 4.526320424, "max_metric_value": 5.455863478, "training_avg": 4.991091951, "training_stddev": 0.1549238424, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last variance value is 4.748. The average for this metric is 4.991.", "is_anomalous": false}, {"value": 5.100341898, "average": 4.997161392, "min_value": 4.539696939, "max_value": 5.454625845, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "2f2a62cba26300bf0aa845b59b1feb15", "metric_id": "edf2abf191c0e0015ca2eec4f0b1eaea", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "variance", "anomaly_score": 0.6766460565, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 5.100341898, "min_metric_value": 4.539696939, "max_metric_value": 5.454625845, "training_avg": 4.997161392, "training_stddev": 0.1524881511, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last variance value is 5.1. The average for this metric is 4.997.", "is_anomalous": false}, {"value": 4.795558104, "average": 4.986550693, "min_value": 4.520825694, "max_value": 5.452275692, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "ffcceacd6921076f2bf8cef35504166e", "metric_id": "3d9c8dbee3626bc46ec591719479da34", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "variance", "anomaly_score": -1.230292058, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 4.795558104, "min_metric_value": 4.520825694, "max_metric_value": 5.452275692, "training_avg": 4.986550693, "training_stddev": 0.1552416664, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last variance value is 4.796. The average for this metric is 4.987.", "is_anomalous": false}, {"value": 4.909776633, "average": 4.98271199, "min_value": 4.526492292, "max_value": 5.438931688, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "7f80c42fa45db4d98f7c8e7776b7018f", "metric_id": "c780a903829fbf30e49a3e26f4f9cd3b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "variance", "anomaly_score": -0.4796068006, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 4.909776633, "min_metric_value": 4.526492292, "max_metric_value": 5.438931688, "training_avg": 4.98271199, "training_stddev": 0.1520732326, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last variance value is 4.91. The average for this metric is 4.983.", "is_anomalous": false}, {"value": 4.880547152, "average": 4.977846998, "min_value": 4.528177261, "max_value": 5.427516734, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "72172297f6f66316573f2289bc66ba5a", "metric_id": "816cca31906a601e1b68b57c2c929ff0", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "variance", "anomaly_score": -0.6491420555, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 4.880547152, "min_metric_value": 4.528177261, "max_metric_value": 5.427516734, "training_avg": 4.977846998, "training_stddev": 0.1498899123, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last variance value is 4.881. The average for this metric is 4.978.", "is_anomalous": false}, {"value": 4.721184699, "average": 4.966180529, "min_value": 4.49764745, "max_value": 5.434713609, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "3598d59a00720e9f93aa36640c7c83cf", "metric_id": "bb569c366f647c6aae4619445d63f0a6", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "variance", "anomaly_score": -1.568699252, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 4.721184699, "min_metric_value": 4.49764745, "max_metric_value": 5.434713609, "training_avg": 4.966180529, "training_stddev": 0.1561776933, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last variance value is 4.721. The average for this metric is 4.966.", "is_anomalous": false}, {"value": 4.717707935, "average": 4.955377373, "min_value": 4.471948462, "max_value": 5.438806284, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "638baeb6315db8f94a2a21bee855f3f8", "metric_id": "01d77ad19c957f7d072070d6c922b131", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "variance", "anomaly_score": -1.474897958, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 4.717707935, "min_metric_value": 4.471948462, "max_metric_value": 5.438806284, "training_avg": 4.955377373, "training_stddev": 0.1611429703, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last variance value is 4.718. The average for this metric is 4.955.", "is_anomalous": false}, {"value": 4.926220804, "average": 4.954162516, "min_value": 4.481022707, "max_value": 5.427302325, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "2ff06ae888082dbf899827a064be3a71", "metric_id": "6c7ccfc3436256232ce444fd5b43a38b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "variance", "anomaly_score": -0.1771677962, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 4.926220804, "min_metric_value": 4.481022707, "max_metric_value": 5.427302325, "training_avg": 4.954162516, "training_stddev": 0.1577132698, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last variance value is 4.926. The average for this metric is 4.954.", "is_anomalous": false}, {"value": 5.034957451, "average": 4.957394313, "min_value": 4.491686528, "max_value": 5.423102099, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "814f1065128040c1c262e4525f149102", "metric_id": "b061d8906f1423d65385343fc1a76722", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "variance", "anomaly_score": 0.4996468162, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 5.034957451, "min_metric_value": 4.491686528, "max_metric_value": 5.423102099, "training_avg": 4.957394313, "training_stddev": 0.1552359285, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last variance value is 5.035. The average for this metric is 4.957.", "is_anomalous": false}, {"value": 4.787438482, "average": 4.950857551, "min_value": 4.483731186, "max_value": 5.417983915, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "30d0345ba727a55f84493802d90a5b10", "metric_id": "84caee92e06267833a61bf19b72d7e11", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "variance", "anomaly_score": -1.049517309, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 4.787438482, "min_metric_value": 4.483731186, "max_metric_value": 5.417983915, "training_avg": 4.950857551, "training_stddev": 0.1557087882, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last variance value is 4.787. The average for this metric is 4.951.", "is_anomalous": false}, {"value": 5.169258666, "average": 4.958946481, "min_value": 4.483852654, "max_value": 5.434040308, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "d51daf61da87b2688e3b1925a4f29feb", "metric_id": "a2f7ebddc255599a38bf127d381e75f1", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "variance", "anomaly_score": 1.32802516, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 5.169258666, "min_metric_value": 4.483852654, "max_metric_value": 5.434040308, "training_avg": 4.958946481, "training_stddev": 0.1583646089, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last variance value is 5.169. The average for this metric is 4.959.", "is_anomalous": false}, {"value": 4.940598998, "average": 4.958291214, "min_value": 4.491962399, "max_value": 5.424620029, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "baa1dbf3668f17986d022723dbba875b", "metric_id": "209ac60d7b8827278002486d1613a687", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "variance", "anomaly_score": -0.1138180702, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 4.940598998, "min_metric_value": 4.491962399, "max_metric_value": 5.424620029, "training_avg": 4.958291214, "training_stddev": 0.1554429383, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last variance value is 4.941. The average for this metric is 4.958.", "is_anomalous": false}, {"value": 4.64656634, "average": 4.94754208, "min_value": 4.457794192, "max_value": 5.437289969, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "d1c79fb91b93ff5ac503ff87d805112b", "metric_id": "0a5ec6e7fbcb8afa352243b7a5d86a1e", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "variance", "anomaly_score": -1.8436572, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 4.64656634, "min_metric_value": 4.457794192, "max_metric_value": 5.437289969, "training_avg": 4.94754208, "training_stddev": 0.1632492962, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last variance value is 4.647. The average for this metric is 4.948.", "is_anomalous": false}, {"value": 5.206309857, "average": 4.956167673, "min_value": 4.454500024, "max_value": 5.457835322, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "38c6effcbb8ab04ac61d87b20f6987fb", "metric_id": "e5f62b22c74f2c5fe55d25df29a64c21", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "variance", "anomaly_score": 1.495863956, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 5.206309857, "min_metric_value": 4.454500024, "max_metric_value": 5.457835322, "training_avg": 4.956167673, "training_stddev": 0.1672225497, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last variance value is 5.206. The average for this metric is 4.956.", "is_anomalous": false}], "result_description": "In column NULL_PERCENT_FLOAT, the last variance value is 5.206. The average for this metric is 4.956."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "min", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('min' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_FLOAT, the last min value is 1.205. The average for this metric is 1.205.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('min' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Min", "metrics": [{"value": 1.200986694, "average": 1.201016924, "min_value": 1.200888669, "max_value": 1.20114518, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "e4abca8ba5242faa9bac7ef85a9f7dd7", "metric_id": "820bfd9c8e4f45a43f2bb28821764e3c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "min", "anomaly_score": -0.7071068888, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 1.200986694, "min_metric_value": 1.200888669, "max_metric_value": 1.20114518, "training_avg": 1.201016924, "training_stddev": 4.275185476e-05, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last min value is 1.201. The average for this metric is 1.201.", "is_anomalous": false}, {"value": 1.208026482, "average": 1.203353444, "min_value": 1.191212195, "max_value": 1.215494693, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "00cdf3b61799adf8cba682382977ae5d", "metric_id": "c5641cbb62fb2c66253492d0bcdfaa44", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "min", "anomaly_score": 1.154668325, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 1.208026482, "min_metric_value": 1.191212195, "max_metric_value": 1.215494693, "training_avg": 1.203353444, "training_stddev": 0.004047083008, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last min value is 1.208. The average for this metric is 1.203.", "is_anomalous": false}, {"value": 1.200875043, "average": 1.202733844, "min_value": 1.192146406, "max_value": 1.213321281, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "154efad7398be763e7ce2718c232ff88", "metric_id": "c276faf96f067c509ed32668dd89ab55", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "min", "anomaly_score": -0.5266997739, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 1.200875043, "min_metric_value": 1.192146406, "max_metric_value": 1.213321281, "training_avg": 1.202733844, "training_stddev": 0.003529145956, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last min value is 1.201. The average for this metric is 1.203.", "is_anomalous": false}, {"value": 1.20896393, "average": 1.203979861, "min_value": 1.191572785, "max_value": 1.216386937, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "091e450dfde420fcca61bf3d9d9be12a", "metric_id": "07ebe6a9448e8921f5a0ca9c20fa6fd2", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "min", "anomaly_score": 1.205135463, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 1.20896393, "min_metric_value": 1.191572785, "max_metric_value": 1.216386937, "training_avg": 1.203979861, "training_stddev": 0.004135692064, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last min value is 1.209. The average for this metric is 1.204.", "is_anomalous": false}, {"value": 1.206323518, "average": 1.20437047, "min_value": 1.192908031, "max_value": 1.21583291, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "a58791a64c76aedb0fe82e0f403f2770", "metric_id": "773dec0a28cab3f3d5dc985ee08644c4", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "min", "anomaly_score": 0.5111602517, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 1.206323518, "min_metric_value": 1.192908031, "max_metric_value": 1.21583291, "training_avg": 1.20437047, "training_stddev": 0.003820813281, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last min value is 1.206. The average for this metric is 1.204.", "is_anomalous": false}, {"value": 1.204517887, "average": 1.20439153, "min_value": 1.193926467, "max_value": 1.214856593, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "e93d29aa737b720f42dd7f86703a191a", "metric_id": "afedf453a30a71783b0f79df65618652", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "min", "anomaly_score": 0.0362224103, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 1.204517887, "min_metric_value": 1.193926467, "max_metric_value": 1.214856593, "training_avg": 1.20439153, "training_stddev": 0.003488354378, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last min value is 1.205. The average for this metric is 1.204.", "is_anomalous": false}, {"value": 1.200841166, "average": 1.203947734, "min_value": 1.193552885, "max_value": 1.214342584, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "b2ca7e64c82b93dac9dbed3fe1e90808", "metric_id": "8e8a13213778485220d380d2ad7af8a5", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "min", "anomaly_score": -0.8965693917, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 1.200841166, "min_metric_value": 1.193552885, "max_metric_value": 1.214342584, "training_avg": 1.203947734, "training_stddev": 0.003464949865, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last min value is 1.201. The average for this metric is 1.204.", "is_anomalous": false}, {"value": 1.204747635, "average": 1.204036612, "min_value": 1.194280275, "max_value": 1.21379295, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "8bbf83a4b6779e8db81c6657aa2ecec7", "metric_id": "ca376c460ea4c07ed3eb0b270487b4cf", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "min", "anomaly_score": 0.2186341826, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 1.204747635, "min_metric_value": 1.194280275, "max_metric_value": 1.21379295, "training_avg": 1.204036612, "training_stddev": 0.003252112599, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last min value is 1.205. The average for this metric is 1.204.", "is_anomalous": false}, {"value": 1.205577428, "average": 1.204190694, "min_value": 1.194876909, "max_value": 1.213504479, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "46531f084a497c3582ec11cbb28b9e4d", "metric_id": "b9e4ae38c40b2457813bcef7de5f3606", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "min", "anomaly_score": 0.4466715724, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 1.205577428, "min_metric_value": 1.194876909, "max_metric_value": 1.213504479, "training_avg": 1.204190694, "training_stddev": 0.00310459504, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last min value is 1.206. The average for this metric is 1.204.", "is_anomalous": false}, {"value": 1.203497484, "average": 1.204127675, "min_value": 1.195269622, "max_value": 1.212985728, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "eee30e0047ae8556ce349c2823126e84", "metric_id": "0605cb418cca2b8f6a1c3a4748985cb1", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "min", "anomaly_score": -0.2134298446, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 1.203497484, "min_metric_value": 1.195269622, "max_metric_value": 1.212985728, "training_avg": 1.204127675, "training_stddev": 0.002952684337, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last min value is 1.203. The average for this metric is 1.204.", "is_anomalous": false}, {"value": 1.220503885, "average": 1.205492359, "min_value": 1.188985782, "max_value": 1.221998936, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "e2d4c442d8057fa36bfe4b60b40122a0", "metric_id": "65acb10f3f6155f8cdfcfce57579bc4b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "min", "anomaly_score": 2.728280721, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 1.220503885, "min_metric_value": 1.188985782, "max_metric_value": 1.221998936, "training_avg": 1.205492359, "training_stddev": 0.00550219245, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last min value is 1.221. The average for this metric is 1.205.", "is_anomalous": false}, {"value": 1.202608379, "average": 1.205270514, "min_value": 1.189285532, "max_value": 1.221255497, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "6d64c217e485b862977f9db4b0c03bc0", "metric_id": "0b0e84c3a41a17d3dfcaa56a9748e35d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "min", "anomaly_score": -0.4996193505, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 1.202608379, "min_metric_value": 1.189285532, "max_metric_value": 1.221255497, "training_avg": 1.205270514, "training_stddev": 0.005328327484, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last min value is 1.203. The average for this metric is 1.205.", "is_anomalous": false}, {"value": 1.215194602, "average": 1.205979378, "min_value": 1.188682623, "max_value": 1.223276132, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "cde68ed2ef898763b02a388fafafca9a", "metric_id": "cc32d48cfba9fe57eff4ddd2a9239e73", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "min", "anomaly_score": 1.598315677, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 1.215194602, "min_metric_value": 1.188682623, "max_metric_value": 1.223276132, "training_avg": 1.205979378, "training_stddev": 0.005765584798, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last min value is 1.215. The average for this metric is 1.206.", "is_anomalous": false}, {"value": 1.200964747, "average": 1.205645069, "min_value": 1.188530871, "max_value": 1.222759268, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "ae6315bd9438a79dbc559d3efefec572", "metric_id": "e3dc9a6f8cafe2dff39e1ced2a506c65", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "min", "anomaly_score": -0.8204279678, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 1.200964747, "min_metric_value": 1.188530871, "max_metric_value": 1.222759268, "training_avg": 1.205645069, "training_stddev": 0.005704732841, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last min value is 1.201. The average for this metric is 1.206.", "is_anomalous": false}, {"value": 1.204255485, "average": 1.20555822, "min_value": 1.18899152, "max_value": 1.22212492, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "82dad0b19355930c95883f40aac09637", "metric_id": "00a55ed01e6db52b614ad7aadd32cc2a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "min", "anomaly_score": -0.2359072396, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 1.204255485, "min_metric_value": 1.18899152, "max_metric_value": 1.22212492, "training_avg": 1.20555822, "training_stddev": 0.005522233451, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last min value is 1.204. The average for this metric is 1.206.", "is_anomalous": false}, {"value": 1.205646955, "average": 1.20556344, "min_value": 1.189522671, "max_value": 1.221604208, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "459e06da32ce836cbea076f2a24b1072", "metric_id": "61699bb11040bd11064469c3e399c34e", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "min", "anomaly_score": 0.01561924775, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 1.205646955, "min_metric_value": 1.189522671, "max_metric_value": 1.221604208, "training_avg": 1.20556344, "training_stddev": 0.005346922859, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last min value is 1.206. The average for this metric is 1.206.", "is_anomalous": false}, {"value": 1.205280726, "average": 1.205547733, "min_value": 1.189984618, "max_value": 1.221110849, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "14707e51b643710940a43a61c0f03bfa", "metric_id": "7fa22cbbe4005b6b8e0e8c0d412f0f0a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "min", "anomaly_score": -0.05146930567, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 1.205280726, "min_metric_value": 1.189984618, "max_metric_value": 1.221110849, "training_avg": 1.205547733, "training_stddev": 0.005187705101, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last min value is 1.205. The average for this metric is 1.206.", "is_anomalous": false}, {"value": 1.202111285, "average": 1.205366868, "min_value": 1.190058431, "max_value": 1.220675304, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "52b310d2674f52643ff2dd6e4241aa1a", "metric_id": "99f5fb75cd811edf371c72557880a889", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "min", "anomaly_score": -0.6379977076, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 1.202111285, "min_metric_value": 1.190058431, "max_metric_value": 1.220675304, "training_avg": 1.205366868, "training_stddev": 0.005102812256, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last min value is 1.202. The average for this metric is 1.205.", "is_anomalous": false}, {"value": 1.200096802, "average": 1.205103364, "min_value": 1.189789572, "max_value": 1.220417157, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "7618a2d680bdcf4d9feef1b628e0e2d3", "metric_id": "28db53fe779638e3eef9816d0599dfff", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "min", "anomaly_score": -0.9807947151, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 1.200096802, "min_metric_value": 1.189789572, "max_metric_value": 1.220417157, "training_avg": 1.205103364, "training_stddev": 0.005104597426, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last min value is 1.2. The average for this metric is 1.205.", "is_anomalous": false}, {"value": 1.200110178, "average": 1.204865594, "min_value": 1.189585812, "max_value": 1.220145375, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "e623db941331c48b68ad7936fe35d959", "metric_id": "85fb92e4870b6cc7675e9a45463e8824", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "min", "anomaly_score": -0.9336682972, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 1.200110178, "min_metric_value": 1.189585812, "max_metric_value": 1.220145375, "training_avg": 1.204865594, "training_stddev": 0.005093260559, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last min value is 1.2. The average for this metric is 1.205.", "is_anomalous": false}, {"value": 1.203938627, "average": 1.204823459, "min_value": 1.189900137, "max_value": 1.219746781, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "38433aaa9f6db9b31757bce908cbecc3", "metric_id": "922fafae2c1a4db28e935ddb7fad6ec4", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "min", "anomaly_score": -0.1778756696, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 1.203938627, "min_metric_value": 1.189900137, "max_metric_value": 1.219746781, "training_avg": 1.204823459, "training_stddev": 0.004974440731, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last min value is 1.204. The average for this metric is 1.205.", "is_anomalous": false}, {"value": 1.217115175, "average": 1.205357881, "min_value": 1.188874457, "max_value": 1.221841306, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "2630a8d0a35a93ba1fdf0c2c07e48a31", "metric_id": "a12fc61459a64e1f67531cd96c06f1a2", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "min", "anomaly_score": 2.139839423, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 1.217115175, "min_metric_value": 1.188874457, "max_metric_value": 1.221841306, "training_avg": 1.205357881, "training_stddev": 0.005494474895, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last min value is 1.217. The average for this metric is 1.205.", "is_anomalous": false}, {"value": 1.202900356, "average": 1.205255484, "min_value": 1.189064286, "max_value": 1.221446682, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "a407c5a0b01319899861b843d98901db", "metric_id": "1cbc37cc98926cff419ed32d3a2a77c1", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "min", "anomaly_score": -0.4363719565, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 1.202900356, "min_metric_value": 1.189064286, "max_metric_value": 1.221446682, "training_avg": 1.205255484, "training_stddev": 0.005397065962, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last min value is 1.203. The average for this metric is 1.205.", "is_anomalous": false}, {"value": 1.201040857, "average": 1.205086899, "min_value": 1.189036152, "max_value": 1.221137646, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "e9ec874ef08f48434691687c59725d94", "metric_id": "0aba6c5dd6b7c85b696cad8c78f6d4db", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "min", "anomaly_score": -0.7562343031, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 1.201040857, "min_metric_value": 1.189036152, "max_metric_value": 1.221137646, "training_avg": 1.205086899, "training_stddev": 0.00535024898, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last min value is 1.201. The average for this metric is 1.205.", "is_anomalous": false}, {"value": 1.202070733, "average": 1.204970893, "min_value": 1.189144634, "max_value": 1.220797152, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "2d6208a78e893dbdbb67de2b716c9097", "metric_id": "1d8ae2860ebc314f2bd60b1be3b39045", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "min", "anomaly_score": -0.5497496179, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 1.202070733, "min_metric_value": 1.189144634, "max_metric_value": 1.220797152, "training_avg": 1.204970893, "training_stddev": 0.00527541962, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last min value is 1.202. The average for this metric is 1.205.", "is_anomalous": false}, {"value": 1.200894687, "average": 1.204819922, "min_value": 1.18912357, "max_value": 1.220516275, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "5714d26c35dbca5afabc01b80f94dbe2", "metric_id": "fc70b1d9593461f640f30ec773199960", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "min", "anomaly_score": -0.7502191844, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 1.200894687, "min_metric_value": 1.18912357, "max_metric_value": 1.220516275, "training_avg": 1.204819922, "training_stddev": 0.005232117411, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last min value is 1.201. The average for this metric is 1.205.", "is_anomalous": false}, {"value": 1.200907798, "average": 1.204680204, "min_value": 1.189118397, "max_value": 1.22024201, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "df45385432fbe24cc6912bdb6b4729d4", "metric_id": "ba8d6d1f89213adc821ef4cfdde4454b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "min", "anomaly_score": -0.7272431925, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 1.200907798, "min_metric_value": 1.189118397, "max_metric_value": 1.22024201, "training_avg": 1.204680204, "training_stddev": 0.005187268927, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last min value is 1.201. The average for this metric is 1.205.", "is_anomalous": false}, {"value": 1.227831409, "average": 1.205478521, "min_value": 1.189118397, "max_value": 1.22024201, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "7159c45f3412f320c370ab1b739d082d", "metric_id": "f759a5313104d024831be433074b33b5", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "min", "anomaly_score": 3.35352426, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 1.227831409, "min_metric_value": 1.185482046, "max_metric_value": 1.225474996, "training_avg": 1.205478521, "training_stddev": 0.00666549156, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last min value is 1.228. The average for this metric is 1.205.", "is_anomalous": true}, {"value": 1.204770652, "average": 1.205454925, "min_value": 1.185802417, "max_value": 1.225107434, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "3c2612082265797892b45886f8581bd5", "metric_id": "aad9e9a040d2c0a545f34e148300b0b5", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "min", "anomaly_score": -0.1044558708, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 1.204770652, "min_metric_value": 1.185802417, "max_metric_value": 1.225107434, "training_avg": 1.205454925, "training_stddev": 0.006550836094, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last min value is 1.205. The average for this metric is 1.205.", "is_anomalous": false}], "result_description": "In column NULL_COUNT_FLOAT, the last min value is 1.205. The average for this metric is 1.205."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "missing_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('missing_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_STR, the last missing_percent value is 58.333. The average for this metric is 21.43.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('missing_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Missing Percent", "metrics": [{"value": 19.167, "average": 20.6945, "min_value": 14.21386635, "max_value": 27.17513365, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "1b8bf99fa1267c57471ea2840d97fcbd", "metric_id": "fb385cefbc4053a8cc236daeb3e1e1aa", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_percent", "anomaly_score": -0.7071067812, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 19.167, "min_metric_value": 14.21386635, "max_metric_value": 27.17513365, "training_avg": 20.6945, "training_stddev": 2.160211217, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_percent value is 19.167. The average for this metric is 20.695.", "is_anomalous": false}, {"value": 20.278, "average": 20.555666667, "min_value": 15.916731121, "max_value": 25.194602213, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "1224df1b06a2608360453fae1081a3dc", "metric_id": "987132a110ef52a93d9b739c2bca6678", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_percent", "anomaly_score": -0.1795670562, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 20.278, "min_metric_value": 15.916731121, "max_metric_value": 25.194602213, "training_avg": 20.555666667, "training_stddev": 1.546311849, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_percent value is 20.278. The average for this metric is 20.556.", "is_anomalous": false}, {"value": 19.556, "average": 20.30575, "min_value": 16.232056068, "max_value": 24.379443932, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "696fd04ad7489ad05a3525848bbeca51", "metric_id": "f5dd8deb318dd27e5e4c75f030fef33f", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_percent", "anomaly_score": -0.5521401553, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 19.556, "min_metric_value": 16.232056068, "max_metric_value": 24.379443932, "training_avg": 20.30575, "training_stddev": 1.357897977, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_percent value is 19.556. The average for this metric is 20.306.", "is_anomalous": false}, {"value": 21.056, "average": 20.4558, "min_value": 16.787092898, "max_value": 24.124507102, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "1a22d1dae48115b603e3fd313187b787", "metric_id": "99f7201bc5fcb34e2ee71bc659a2afe6", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_percent", "anomaly_score": 0.490799606, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 21.056, "min_metric_value": 16.787092898, "max_metric_value": 24.124507102, "training_avg": 20.4558, "training_stddev": 1.222902367, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_percent value is 21.056. The average for this metric is 20.456.", "is_anomalous": false}, {"value": 20.889, "average": 20.528, "min_value": 17.20399296, "max_value": 23.85200704, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "fdd0dc068a57d8f3a580e2bebd2e5a99", "metric_id": "4467fe2d798a3233ba620c7091ca212c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_percent", "anomaly_score": 0.3258115844, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 20.889, "min_metric_value": 17.20399296, "max_metric_value": 23.85200704, "training_avg": 20.528, "training_stddev": 1.108002347, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_percent value is 20.889. The average for this metric is 20.528.", "is_anomalous": false}, {"value": 19.389, "average": 20.365285714, "min_value": 17.067482939, "max_value": 23.663088489, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "d57b7008c72be0c939d49c773fd05629", "metric_id": "4cced3c90baae6a4133240e4efe1ff30", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_percent", "anomaly_score": -0.8881238032, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 19.389, "min_metric_value": 17.067482939, "max_metric_value": 23.663088489, "training_avg": 20.365285714, "training_stddev": 1.099267592, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_percent value is 19.389. The average for this metric is 20.365.", "is_anomalous": false}, {"value": 21.278, "average": 20.479375, "min_value": 17.276401674, "max_value": 23.682348326, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "2f30ccdcf934d61eabb4e70b2af8f5b7", "metric_id": "5456ae7d2a79f53f7e01c402904a9f17", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_percent", "anomaly_score": 0.7480159078, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 21.278, "min_metric_value": 17.276401674, "max_metric_value": 23.682348326, "training_avg": 20.479375, "training_stddev": 1.067657775, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_percent value is 21.278. The average for this metric is 20.479.", "is_anomalous": false}, {"value": 18.889, "average": 20.302666667, "min_value": 16.910624362, "max_value": 23.694708972, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "70e6082713f266343e8b240e4178b077", "metric_id": "3c500717ba358c375d6e97f6ab791ef8", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_percent", "anomaly_score": -1.250279218, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 18.889, "min_metric_value": 16.910624362, "max_metric_value": 23.694708972, "training_avg": 20.302666667, "training_stddev": 1.130680768, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_percent value is 18.889. The average for this metric is 20.303.", "is_anomalous": false}, {"value": 18.833, "average": 20.1557, "min_value": 16.666940894, "max_value": 23.644459106, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "3d77b69c265d659ec90ebd7440e06962", "metric_id": "fb9c06627ef0452034c620179b6c2b0a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_percent", "anomaly_score": -1.137395813, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 18.833, "min_metric_value": 16.666940894, "max_metric_value": 23.644459106, "training_avg": 20.1557, "training_stddev": 1.162919702, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_percent value is 18.833. The average for this metric is 20.156.", "is_anomalous": false}, {"value": 20.556, "average": 20.192090909, "min_value": 16.862616233, "max_value": 23.521565585, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "7feb4d82df14ad9bfa1644012e8c1c4a", "metric_id": "b55f7bc47474bfc46ffe6dfd56c902fa", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_percent", "anomaly_score": 0.3278977553, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 20.556, "min_metric_value": 16.862616233, "max_metric_value": 23.521565585, "training_avg": 20.192090909, "training_stddev": 1.109824892, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_percent value is 20.556. The average for this metric is 20.192.", "is_anomalous": false}, {"value": 20.389, "average": 20.2085, "min_value": 17.029393565, "max_value": 23.387606435, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "8aee33514e7f0949efc1f94195d46ac6", "metric_id": "a47d63214d86a6bc4e2ed5c760a0c8a8", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_percent", "anomaly_score": 0.170330881, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 20.389, "min_metric_value": 17.029393565, "max_metric_value": 23.387606435, "training_avg": 20.2085, "training_stddev": 1.059702145, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_percent value is 20.389. The average for this metric is 20.209.", "is_anomalous": false}, {"value": 20.556, "average": 20.235230769, "min_value": 17.17776585, "max_value": 23.292695688, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "66e6a3cd352d2426adf3a486acb16d74", "metric_id": "6fd46c8eb90e766b9ab54c06dccfbd52", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_percent", "anomaly_score": 0.3147403872, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 20.556, "min_metric_value": 17.17776585, "max_metric_value": 23.292695688, "training_avg": 20.235230769, "training_stddev": 1.019154973, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_percent value is 20.556. The average for this metric is 20.235.", "is_anomalous": false}, {"value": 21.611, "average": 20.3335, "min_value": 17.195703033, "max_value": 23.471296967, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "4265a6975d1e32a1b1e0d67ef6205afd", "metric_id": "10c62b1620af02d4710480c0befe917c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_percent", "anomaly_score": 1.221398338, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 21.611, "min_metric_value": 17.195703033, "max_metric_value": 23.471296967, "training_avg": 20.3335, "training_stddev": 1.045932322, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_percent value is 21.611. The average for this metric is 20.333.", "is_anomalous": false}, {"value": 19.833, "average": 20.300133333, "min_value": 17.2517238, "max_value": 23.348542867, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "05bd90d2ffc09e62c12d19e40a897cde", "metric_id": "632a20d626541b384595e04b0c320a2f", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_percent", "anomaly_score": -0.4597151349, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 19.833, "min_metric_value": 17.2517238, "max_metric_value": 23.348542867, "training_avg": 20.300133333, "training_stddev": 1.016136511, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_percent value is 19.833. The average for this metric is 20.3.", "is_anomalous": false}, {"value": 19.556, "average": 20.253625, "min_value": 17.256166702, "max_value": 23.251083298, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "0ba6448090a94b09d17a11927f1dce8b", "metric_id": "ad42d996c0539ac50a20d3361a2b4d98", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_percent", "anomaly_score": -0.6982165527, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 19.556, "min_metric_value": 17.256166702, "max_metric_value": 23.251083298, "training_avg": 20.253625, "training_stddev": 0.9991527661, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_percent value is 19.556. The average for this metric is 20.254.", "is_anomalous": false}, {"value": 20.222, "average": 20.251764706, "min_value": 17.349396971, "max_value": 23.154132441, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "bc6edbd36195946a9f2151e2690a74b3", "metric_id": "10f87bdd5bcd2a426fdefc0039f135a4", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_percent", "anomaly_score": -0.03076595587, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 20.222, "min_metric_value": 17.349396971, "max_metric_value": 23.154132441, "training_avg": 20.251764706, "training_stddev": 0.9674559117, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_percent value is 20.222. The average for this metric is 20.252.", "is_anomalous": false}, {"value": 20.667, "average": 20.274833333, "min_value": 17.443855655, "max_value": 23.105811012, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "62f2447d879e7bd0b54127231a48f4ff", "metric_id": "50ccdfeae54b4d57df69a44596d6a0d4", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_percent", "anomaly_score": 0.4155808111, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 20.667, "min_metric_value": 17.443855655, "max_metric_value": 23.105811012, "training_avg": 20.274833333, "training_stddev": 0.9436592263, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_percent value is 20.667. The average for this metric is 20.275.", "is_anomalous": false}, {"value": 19.556, "average": 20.237, "min_value": 17.441655477, "max_value": 23.032344523, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "48bcf27ae62653d2f8094f3edd8700bc", "metric_id": "8d1dc96e9463f7dc4b65655fb73422f1", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_percent", "anomaly_score": -0.7308580332, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 19.556, "min_metric_value": 17.441655477, "max_metric_value": 23.032344523, "training_avg": 20.237, "training_stddev": 0.9317815075, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_percent value is 19.556. The average for this metric is 20.237.", "is_anomalous": false}, {"value": 19.722, "average": 20.21125, "min_value": 17.468615925, "max_value": 22.953884075, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "930ca12aa536f12be565f3c4d9d23f60", "metric_id": "d24b5e572b0e8b35f97bd80e9d99d3b1", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_percent", "anomaly_score": -0.5351607104, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 19.722, "min_metric_value": 17.468615925, "max_metric_value": 22.953884075, "training_avg": 20.21125, "training_stddev": 0.9142113584, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_percent value is 19.722. The average for this metric is 20.211.", "is_anomalous": false}, {"value": 21.556, "average": 20.275285714, "min_value": 17.460867865, "max_value": 23.089703563, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "cd47c438ad358cbadabdc4d1e909da1c", "metric_id": "075ee0ce91a66e92d723832bbf05ff70", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_percent", "anomaly_score": 1.36516433, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 21.556, "min_metric_value": 17.460867865, "max_metric_value": 23.089703563, "training_avg": 20.275285714, "training_stddev": 0.938139283, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_percent value is 21.556. The average for this metric is 20.275.", "is_anomalous": false}, {"value": 20.056, "average": 20.265318182, "min_value": 17.515148828, "max_value": 23.015487536, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "1469bc02d7ef919d820e6a5bbf105b18", "metric_id": "a6ab3b16a32b52bf69929abad9f2ad09", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_percent", "anomaly_score": -0.228333046, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 20.056, "min_metric_value": 17.515148828, "max_metric_value": 23.015487536, "training_avg": 20.265318182, "training_stddev": 0.9167231179, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_percent value is 20.056. The average for this metric is 20.265.", "is_anomalous": false}, {"value": 20.056, "average": 20.256217391, "min_value": 17.566090297, "max_value": 22.946344485, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "8b7d9eac7836b340fb9e48d0ba6d7ffd", "metric_id": "e4a096bccf138b59771a7c81b34b0319", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_percent", "anomaly_score": -0.2232802217, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 20.056, "min_metric_value": 17.566090297, "max_metric_value": 22.946344485, "training_avg": 20.256217391, "training_stddev": 0.8967090314, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_percent value is 20.056. The average for this metric is 20.256.", "is_anomalous": false}, {"value": 20.222, "average": 20.254791667, "min_value": 17.623712027, "max_value": 22.885871306, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "f1512294e75945c5456b4dd64a0aed9f", "metric_id": "22db4355b5ec9a4450c132b505fddcef", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_percent", "anomaly_score": -0.03738959419, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 20.222, "min_metric_value": 17.623712027, "max_metric_value": 22.885871306, "training_avg": 20.254791667, "training_stddev": 0.8770265466, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_percent value is 20.222. The average for this metric is 20.255.", "is_anomalous": false}, {"value": 19.833, "average": 20.23792, "min_value": 17.649834571, "max_value": 22.826005429, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "f6b0d2fd016d0a253751fc21f882d1ae", "metric_id": "3d8c590f48525421aa39428e27c8794b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_percent", "anomaly_score": -0.4693662683, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 19.833, "min_metric_value": 17.649834571, "max_metric_value": 22.826005429, "training_avg": 20.23792, "training_stddev": 0.8626951431, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_percent value is 19.833. The average for this metric is 20.238.", "is_anomalous": false}, {"value": 19.167, "average": 20.196730769, "min_value": 17.583829684, "max_value": 22.809631855, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "8d726b7c9b9824502b1d49d64f8bf2cd", "metric_id": "0479e5007116bd001ac7f621ef5086c9", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_percent", "anomaly_score": -1.182284444, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 19.167, "min_metric_value": 17.583829684, "max_metric_value": 22.809631855, "training_avg": 20.196730769, "training_stddev": 0.8709670284, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_percent value is 19.167. The average for this metric is 20.197.", "is_anomalous": false}, {"value": 19.222, "average": 20.16062963, "min_value": 17.537393939, "max_value": 22.78386532, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "a7f6efcaf733461aa8814a3527c1f2d6", "metric_id": "d4cf72dda3ddfda3494ab9ce04668631", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_percent", "anomaly_score": -1.073441056, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 19.222, "min_metric_value": 17.537393939, "max_metric_value": 22.78386532, "training_avg": 20.16062963, "training_stddev": 0.8744118968, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_percent value is 19.222. The average for this metric is 20.161.", "is_anomalous": false}, {"value": 19.222, "average": 20.127107143, "min_value": 17.498478814, "max_value": 22.755735472, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "9bb95c50a4a5726e9f65fb28694df112", "metric_id": "3587284b83b0746cd856d75a4fd9c130", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_percent", "anomaly_score": -1.032980357, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 19.222, "min_metric_value": 17.498478814, "max_metric_value": 22.755735472, "training_avg": 20.127107143, "training_stddev": 0.8762094431, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_percent value is 19.222. The average for this metric is 20.127.", "is_anomalous": false}, {"value": 21.0, "average": 20.157206897, "min_value": 17.530540359, "max_value": 22.783873434, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "aa2484d68e2c92752f2cd17548d52314", "metric_id": "bb5f9dfaaa4f64bd5698e1939de7e8b9", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_percent", "anomaly_score": 0.9625810029, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 21.0, "min_metric_value": 17.530540359, "max_metric_value": 22.783873434, "training_avg": 20.157206897, "training_stddev": 0.8755555126, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_percent value is 21. The average for this metric is 20.157.", "is_anomalous": false}, {"value": 58.333, "average": 21.429733333, "min_value": 17.530540359, "max_value": 22.783873434, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "e6b76b39fe638399faf6d7a4f749cc63", "metric_id": "84ca9b0ff1a7877850fb87d4030a31a6", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "missing_percent", "anomaly_score": 5.254771722, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 58.333, "min_metric_value": 0.3613014679, "max_metric_value": 42.498165199, "training_avg": 21.429733333, "training_stddev": 7.022810622, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last missing_percent value is 58.333. The average for this metric is 21.43.", "is_anomalous": true}], "result_description": "In column NULL_PERCENT_STR, the last missing_percent value is 58.333. The average for this metric is 21.43."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "zero_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('zero_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_FLOAT, the last zero_percent value is 80. The average for this metric is 5.567.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('zero_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Zero Percent", "metrics": [{"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "67b5be9dc59b6e9340f1c1fd93f9e7fb", "metric_id": "f039332f31520ea6c59c561bb91b3695", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "277961823269839f78731bd7fd100ef6", "metric_id": "2184a36f936118e4cf9c6a1d91636ce4", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "a2a14922fb694691360bfb329d60f95f", "metric_id": "7571fbd724c328ae027ec1c212280af9", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "e922b4fb85ce6e7e0daa78336a4e210e", "metric_id": "77d0a6424708f91116658248aa0289e1", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "a9f9624baec33e6d988218e4e63e6cd3", "metric_id": "d2344b520f2363587aefb7d16e581080", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "26cdb5ec6c9c5e5d87d77b3bf04ea8b8", "metric_id": "81cf12f03c8e5a5cab4d05020b0ebce9", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "e10f1798f1798cd8d25257330f013632", "metric_id": "bec3527dcbe8bac0c43eb97eee748282", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "1be1e57b10a8c98df1ea6633f740ec34", "metric_id": "3cf2ee8152ba8cb13a996f240cdbc04e", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "72c48b040cdd15fe00d1a42e3cc27e11", "metric_id": "61992294523161f71dcec8fd0fcf1c80", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "e35de7b2661f4d234a09c9ae6e49e841", "metric_id": "1014ff88661260afd6dbbf730876fcfc", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "922e0fc923c3794130fc6969aedd8ad1", "metric_id": "800655bc61f3a156d356cfd57a03329b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "78992c8f5bb044989374576f48f07952", "metric_id": "bb23576d1fd667cd5aae60f6d8312cbc", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "5fd7ba4d4a82c46fefdd1e92b493d808", "metric_id": "c5e1e9b89093837ed002c1e96fc57d40", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "b1edff879a5e923452306f0562531587", "metric_id": "5c9a03d512a9d82fbc9a3f0e66873504", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "495dc3288940171a6b521834a2c1f795", "metric_id": "ddc06bcc23ccf0909ca76c3c51a4e847", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "1cfbc19299991b97a814ed59ab51fb91", "metric_id": "90c3aae603eec1c311c51c8c1ed2dccd", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "46a6b3d84432d66923af7da2cb7ea777", "metric_id": "d1805bbc961175606b3276a02619bccc", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "7f6056e16e407529da0627ecad0cd573", "metric_id": "df9bb538e89ea3883d57372c75a2f864", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "285ca890d1f5313a49add633abc3b2e7", "metric_id": "f3add25f731a41c94cddb0c769aa662f", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "299f46ffcad079998bbbafff02ca409f", "metric_id": "65d7a7abf668e8be8d10a737416ef8ed", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "68030422cc34705bf61bfaf3c614eb87", "metric_id": "4196c119786457e0833007c95a2d4737", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "4ca7481629b94974ea636b7cc5fc5630", "metric_id": "91471b6a4faa356edfc2427463d385fc", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "ac5fa4e3139da97aa3432e42d7b972ef", "metric_id": "181ad99464a16a5f27d2ab80da376003", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "3508dcb6eab0927f5cd2e15a5b9b2675", "metric_id": "1dbe3a3d12391ee51f229dc79762b27a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "f6a373c7d0a2af586539f481e8b54ab4", "metric_id": "58f23c92aa3679378e6897151e1916fe", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "25ca219ddb06306465bb7a4b3063fe09", "metric_id": "7bcf4c629e7f276bc9883dd07add9c55", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "d7eb8feeb8d2930c70bf54c22a9702d3", "metric_id": "cea574b8cd7f50b6bfa6b3ac6cf88a6d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "0e46d7c5248bc71d3af44fe6dd47b3c6", "metric_id": "328bdf32765874857b185c98e3a9e3ed", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 80.0, "average": 5.566666667, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "b8c3c46abbbc96bc841998bbe4081c2b", "metric_id": "7dc58d537ee5f417deb6b060eaeb5180", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_percent", "anomaly_score": 5.294651389, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 80.0, "min_metric_value": -36.607970261, "max_metric_value": 47.741303595, "training_avg": 5.566666667, "training_stddev": 14.058212309, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_percent value is 80. The average for this metric is 5.567.", "is_anomalous": true}], "result_description": "In column NULL_COUNT_FLOAT, the last zero_percent value is 80. The average for this metric is 5.567."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_BOOL", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_BOOL' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_BOOL, the last null_count value is 240. The average for this metric is 58.7.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_BOOL' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Null Count", "metrics": [{"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "4d27f26662994ba3ceace0dc109af457", "metric_id": "039c9ea95fa58daedc7b27e6f7f8c4f7", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "1a902264271389e49d58aa892b8eb040", "metric_id": "8bccde56129b127bed763381e143383e", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "f93d3aa4a877893f55d20febf15cfc45", "metric_id": "1cf8a0601ef70b15015da4c878b22945", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "b06f784d3f038c280fa78aeb17687629", "metric_id": "42e16e675d54a0a095b068a1a73a4167", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "8e93b68f08113107dfe4564aff98ef87", "metric_id": "38139c68adc8a3ff06da76501c9a57f6", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "2ccde9224b67cbd4112fb03c51c232ff", "metric_id": "3c70952189726ef11516968cd5103412", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "017a9a066e7a2c3223eb38087bcc606f", "metric_id": "818f512dd8edca178d496a35fde335a8", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "8068b41928d1c3438a5951ad4bf8a3fb", "metric_id": "6f7b8d6368142d774ca0f13ef0001567", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "4aaae8f5b862c8d77b4a109b22a8d9ac", "metric_id": "7a0b141366e8675c390714a55d9a3a9d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "927d1daab0a281551636e87ff6d31403", "metric_id": "f22e2bb7cc74a9418337a17b776cd931", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "737712fc46d1e28144c3486afdc1f929", "metric_id": "757c41a215737cc4b75aacc7554e4cae", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "537151bde7747de150fafdfaf1820c53", "metric_id": "29049b9f7f0c70efc694dc08c8b43d38", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "c0af2a7299f3a91023dbe92ba885bd8e", "metric_id": "dcf9df2ec1e02f0eabb7688f33cca8d3", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "4baaaa8e824a85c6028edcb92e57f458", "metric_id": "fc0598368473971916e33b71b13f90d7", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "a8a9126dd3ba1e70fc57ed3309d5536b", "metric_id": "26ccaef12beda1e2996c244014f748cf", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "6d8f1dc3a498fbb0c49b476095f2ea34", "metric_id": "0bb368f6cb923a256463d90de449dba2", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "c67187eb9fdc57f54c913493cd1c9fbb", "metric_id": "c796cc60d15ddd38d08869cfa23fd0ee", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "734f59123100aa46b6b51287f37ec27d", "metric_id": "2e5746f22924f829cdc74915bf0f9ef4", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "127af77bd3d109c052745f2ba37fb09a", "metric_id": "14be703fb1db64dc6965820623b26ac5", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "e5dc8130a6d309ea76782ce97d8d941a", "metric_id": "5fdedef49c9d3a0fc9431dc39cb2300e", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "57b43d0e0d53ed54efe872934982a40d", "metric_id": "f7a232e75f62219829cf01aadf18ab44", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "fa95f28df3407c495324d573c8d79146", "metric_id": "77bc57ce3ef4d04664683c0ba253e254", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "6e62af2c8d2996457f85aa4aa18baa3f", "metric_id": "f6e2d9700441f62db7a2011a9767358b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "eb18b25ee4b05b5f63d2fbaf68c0ae75", "metric_id": "79b41a7a254406fd8eac104e81300a89", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "7b6a827f68e9b946d74a36fdcb4ba08e", "metric_id": "3757ba45ab66791283e158720a98db7b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "410547b5f4e95595972c26a341496cfb", "metric_id": "40e9cd440b7e44a8a1a3cc7462218f04", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "d17a148880f53a1918df34569fcbd653", "metric_id": "c42d93637e47b3483f79de6b46f5ad00", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 9.0, "average": 52.448275862, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "aedfd818106df1f2fc8bdd9050ab53a3", "metric_id": "87300bdc2475c277d2cee23d5eb5fa5b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_count", "anomaly_score": -5.199469469, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 9.0, "min_metric_value": 27.379405208, "max_metric_value": 77.517146516, "training_avg": 52.448275862, "training_stddev": 8.356290218, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_count value is 9. The average for this metric is 52.448.", "is_anomalous": true}, {"value": 240.0, "average": 58.7, "min_value": 27.379405208, "max_value": 77.517146516, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "40639e526a638456004e3f77a4421b5d", "metric_id": "044afc317d244fa5c26c11d9d7d706d1", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_BOOL", "metric_name": "null_count", "anomaly_score": 5.148695731, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 240.0, "min_metric_value": -46.938404067, "max_metric_value": 164.338404067, "training_avg": 58.7, "training_stddev": 35.212801356, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_BOOL, the last null_count value is 240. The average for this metric is 58.7.", "is_anomalous": true}], "result_description": "In column NULL_COUNT_BOOL, the last null_count value is 240. The average for this metric is 58.7."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "zero_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('zero_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_FLOAT, the last zero_count value is 240. The average for this metric is 58.7.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('zero_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Zero Count", "metrics": [{"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "dd14ac4bbf8177e35e186430f29f2034", "metric_id": "0ec5427105f74d5baea05f0ac05c07c3", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "9cbb27d81e797eb37c1a78377e0ced20", "metric_id": "4525267b194b1bc647996f32a1e0ac52", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "148d86e294af9b2a6aca53863169089a", "metric_id": "a0fb013665529c90c546f4244d5c150b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "1a4f0ba3955f3a2b6a7114c2dfdb7b5e", "metric_id": "35be82f911bcae54af404a89e5143914", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "b20c0119654840e1997b359defb98069", "metric_id": "43105df56f910dd112e101b982347ded", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "707b78b7153b0413b16106da213acd21", "metric_id": "598040be88ff26a3860d314978999230", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "94e67e7303d992c93383adccb57dd26d", "metric_id": "b44d4c1a683e4d8adf4e2b67f6b227e7", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "ad5662476395cfc4d6978e3433f8997f", "metric_id": "7328dcda4366ce7aaf820b1766e0c1ff", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "9f4518f468ed04006de5b4497b3ecdca", "metric_id": "55aef2211631258154e11e5d7ede0854", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "2c95e0c5c0a027f5c070a887f06aed20", "metric_id": "f4ac75676c8a5b24b5f44050b04abe43", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "d50528e3d747c34e460a0f0a2cda86d1", "metric_id": "fdad0c57733d8d8d4e3aac73576891ea", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "4ccebdd2b80040d63205d233dbdcacbe", "metric_id": "aa374aac4c555dbc156527152e5243ad", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "e1b616ff3128d6b5e99643f31f7a4751", "metric_id": "7e590351859a3c675b0fb6c378f4fe6d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "1cefb615839e1e8b9cef20e3e645bff3", "metric_id": "ce472055d2062f30cf41fa10071a8a49", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "22843e40e8793b31261e6671aaba051c", "metric_id": "45af0cd405d189f755ba77bd135a0d51", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "699d36834b3c730a72d1cd8d3154324d", "metric_id": "af3a2e7eb7e403690272b82c726554e3", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "38d1088bc60d3e6ef3e1c0849fadcc8a", "metric_id": "3e93fb6a40086b9def614014ae404bf6", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "70de4d8b44c857e3c017e030b33ad598", "metric_id": "31f9554066eb6bd5f246624ae7bd68ea", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "1c3927d86fded13047364e2f1f0b835c", "metric_id": "0364d7f00cdb52473af255bf14f2c25f", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "cbb2175e0461611abf3d46a8aeacf8d1", "metric_id": "bd21a0b5e0123d71f9d25ac78bc6e6bb", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "6841928675a0c75f8c393481a57a5f7a", "metric_id": "f41f1e2bf50ceb1603f7f9710a4db292", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "6851e944fb051bf38ee95a093f37743a", "metric_id": "82c06c279d665e630b84dafaf6331ac8", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "19e52fb703967625aeeb5619569164ef", "metric_id": "8174c5962f5e6c607fc0a38a1816e6b8", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "0614a9731b40e8025898088bef86b993", "metric_id": "430e2560067b61e50377547b926b876b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "adef2a5cb9e6fea70b01fc3cb48a825f", "metric_id": "5c934df6043d83d21963d625d857422f", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "22695037c231b652dca07270621c3f6c", "metric_id": "d2cf01fe037d0a9a9a57a889e2ca8285", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "e0bc2c9b3386f4a6bbd515c3e6c736d7", "metric_id": "c33946607c9f126fe45ec5115d1dea88", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 9.0, "average": 52.448275862, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "61987a8a757e6b14c6d738d2a880a7da", "metric_id": "c8af137eb22d34bea8a359c09198b4b9", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_count", "anomaly_score": -5.199469469, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 9.0, "min_metric_value": 27.379405208, "max_metric_value": 77.517146516, "training_avg": 52.448275862, "training_stddev": 8.356290218, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_count value is 9. The average for this metric is 52.448.", "is_anomalous": true}, {"value": 240.0, "average": 58.7, "min_value": 27.379405208, "max_value": 77.517146516, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "501c1d10e7fc8577808490764e7844d6", "metric_id": "b2bf1b7906ee6a3f782ad89105e5a373", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "zero_count", "anomaly_score": 5.148695731, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 240.0, "min_metric_value": -46.938404067, "max_metric_value": 164.338404067, "training_avg": 58.7, "training_stddev": 35.212801356, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last zero_count value is 240. The average for this metric is 58.7.", "is_anomalous": true}], "result_description": "In column NULL_COUNT_FLOAT, the last zero_count value is 240. The average for this metric is 58.7."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_INT, the last null_count value is 204. The average for this metric is 342.5.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Null Count", "metrics": [{"value": 351.0, "average": 357.0, "min_value": 331.544155877, "max_value": 382.455844123, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "fc4f1ee6b638f0ae551c229573b0eed3", "metric_id": "bf4355437d4bcb0484bdce66043a5be1", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_count", "anomaly_score": -0.7071067812, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 351.0, "min_metric_value": 331.544155877, "max_metric_value": 382.455844123, "training_avg": 357.0, "training_stddev": 8.485281374, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_count value is 351. The average for this metric is 357.", "is_anomalous": false}, {"value": 374.0, "average": 362.666666667, "min_value": 328.155798813, "max_value": 397.17753452, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "7f4b6a57a276946e359742bf76968be8", "metric_id": "1dd9b4fdc41a6637733a6e279da9aef6", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_count", "anomaly_score": 0.9851968993, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 374.0, "min_metric_value": 328.155798813, "max_metric_value": 397.17753452, "training_avg": 362.666666667, "training_stddev": 11.503622618, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_count value is 374. The average for this metric is 362.667.", "is_anomalous": false}, {"value": 370.0, "average": 364.5, "min_value": 334.251033075, "max_value": 394.748966925, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "5de86bbbbbfb0e1cc6a9aa907b032ee8", "metric_id": "c64a85cfc26aaae0570e4ea7e459c7f7", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_count", "anomaly_score": 0.545473174, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 370.0, "min_metric_value": 334.251033075, "max_metric_value": 394.748966925, "training_avg": 364.5, "training_stddev": 10.082988975, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_count value is 370. The average for this metric is 364.5.", "is_anomalous": false}, {"value": 370.0, "average": 365.6, "min_value": 338.384195768, "max_value": 392.815804232, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "cf913de65d7685046fe61c5847ba68da", "metric_id": "86fc16a2a0d6f70c46ee42ba694b3ca9", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_count", "anomaly_score": 0.4850123071, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 370.0, "min_metric_value": 338.384195768, "max_metric_value": 392.815804232, "training_avg": 365.6, "training_stddev": 9.071934744, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_count value is 370. The average for this metric is 365.6.", "is_anomalous": false}, {"value": 378.0, "average": 367.666666667, "min_value": 338.975204522, "max_value": 396.358128811, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "39093b8c92aebca6c93094a566080c65", "metric_id": "0ce6e65731b100f033cceec99dd62ef1", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_count", "anomaly_score": 1.080460795, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 378.0, "min_metric_value": 338.975204522, "max_metric_value": 396.358128811, "training_avg": 367.666666667, "training_stddev": 9.563820715, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_count value is 378. The average for this metric is 367.667.", "is_anomalous": false}, {"value": 372.0, "average": 368.285714286, "min_value": 341.637208565, "max_value": 394.934220006, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "74d16e02279f21f78f7899ff20838890", "metric_id": "e241375c78f9689ad77fa25999b1658a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_count", "anomaly_score": 0.4181419123, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 372.0, "min_metric_value": 341.637208565, "max_metric_value": 394.934220006, "training_avg": 368.285714286, "training_stddev": 8.88283524, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_count value is 372. The average for this metric is 368.286.", "is_anomalous": false}, {"value": 331.0, "average": 363.625, "min_value": 317.012808155, "max_value": 410.237191845, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "1a5f19541d2cde456dda4606018f8fb7", "metric_id": "0cca2d6b667f8d92c43472d3942d1a73", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_count", "anomaly_score": -2.09977253, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 331.0, "min_metric_value": 317.012808155, "max_metric_value": 410.237191845, "training_avg": 363.625, "training_stddev": 15.537397282, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_count value is 331. The average for this metric is 363.625.", "is_anomalous": false}, {"value": 378.0, "average": 365.222222222, "min_value": 319.311983713, "max_value": 411.132460732, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "e68b754f96465f9ec6786439347d1219", "metric_id": "aa8907a3604ea791bdd32ed9b96dd827", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_count", "anomaly_score": 0.8349626266, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 378.0, "min_metric_value": 319.311983713, "max_metric_value": 411.132460732, "training_avg": 365.222222222, "training_stddev": 15.303412837, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_count value is 378. The average for this metric is 365.222.", "is_anomalous": false}, {"value": 366.0, "average": 365.3, "min_value": 322.009123363, "max_value": 408.590876637, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "eb33031e9e6eaa69762992e648c49300", "metric_id": "988778ae12b99d9399bf18e3beb2fd29", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_count", "anomaly_score": 0.04850906619, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 366.0, "min_metric_value": 322.009123363, "max_metric_value": 408.590876637, "training_avg": 365.3, "training_stddev": 14.430292212, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_count value is 366. The average for this metric is 365.3.", "is_anomalous": false}, {"value": 369.0, "average": 365.636363636, "min_value": 324.430891802, "max_value": 406.841835471, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "6bcd135bd7afbb479f8232c8f1ba662c", "metric_id": "900e3aff5005a19df4dc9fb5bb0b11e3", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_count", "anomaly_score": 0.2448924534, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 369.0, "min_metric_value": 324.430891802, "max_metric_value": 406.841835471, "training_avg": 365.636363636, "training_stddev": 13.735157278, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_count value is 369. The average for this metric is 365.636.", "is_anomalous": false}, {"value": 347.0, "average": 364.083333333, "min_value": 321.609544625, "max_value": 406.557122042, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "fedc2c8a38fe4e1c801d5aed0f6c6971", "metric_id": "f752b8322efa391d2c0ee377c284d98b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_count", "anomaly_score": -1.206626523, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 347.0, "min_metric_value": 321.609544625, "max_metric_value": 406.557122042, "training_avg": 364.083333333, "training_stddev": 14.15792957, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_count value is 347. The average for this metric is 364.083.", "is_anomalous": false}, {"value": 352.0, "average": 363.153846154, "min_value": 321.263880401, "max_value": 405.043811907, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "97270e76185cd2a9fbe26741ecba55c5", "metric_id": "a453d7df4ac29d5b28e407bd8e264da2", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_count", "anomaly_score": -0.7987960329, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 352.0, "min_metric_value": 321.263880401, "max_metric_value": 405.043811907, "training_avg": 363.153846154, "training_stddev": 13.963321918, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_count value is 352. The average for this metric is 363.154.", "is_anomalous": false}, {"value": 349.0, "average": 362.142857143, "min_value": 320.326933997, "max_value": 403.958780289, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "07f3167fdeb8a02b38114e822ebb29c3", "metric_id": "c2332549079a3f3ddc64666d9fb1e964", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_count", "anomaly_score": -0.9429080709, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 349.0, "min_metric_value": 320.326933997, "max_metric_value": 403.958780289, "training_avg": 362.142857143, "training_stddev": 13.938641049, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_count value is 349. The average for this metric is 362.143.", "is_anomalous": false}, {"value": 344.0, "average": 360.933333333, "min_value": 318.258148172, "max_value": 403.608518495, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "b2901e0580d54e05c68ec09b84cb30e1", "metric_id": "4031d39082df2900dda95b181bdb2c79", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_count", "anomaly_score": -1.190387337, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 344.0, "min_metric_value": 318.258148172, "max_metric_value": 403.608518495, "training_avg": 360.933333333, "training_stddev": 14.225061721, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_count value is 344. The average for this metric is 360.933.", "is_anomalous": false}, {"value": 347.0, "average": 360.0625, "min_value": 317.530600207, "max_value": 402.594399793, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "98966d38513a1a9f142ee60a4b182821", "metric_id": "ec2b6808cd14f198d39dc9972ee4dfc6", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_count", "anomaly_score": -0.9213672606, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 347.0, "min_metric_value": 317.530600207, "max_metric_value": 402.594399793, "training_avg": 360.0625, "training_stddev": 14.177299931, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_count value is 347. The average for this metric is 360.063.", "is_anomalous": false}, {"value": 358.0, "average": 359.941176471, "min_value": 318.732507337, "max_value": 401.149845604, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "49a3c8de70801883a278a0a1587698b7", "metric_id": "b4efddd523b2b79e8c2624fad4a9d119", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_count", "anomaly_score": -0.1413180657, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 358.0, "min_metric_value": 318.732507337, "max_metric_value": 401.149845604, "training_avg": 359.941176471, "training_stddev": 13.736223045, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_count value is 358. The average for this metric is 359.941.", "is_anomalous": false}, {"value": 345.0, "average": 359.111111111, "min_value": 317.760387382, "max_value": 400.46183484, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "5b00e3c1eceda75a90f01dea9aaca8b1", "metric_id": "52a58810fa9c1c511baac9fabab81305", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_count", "anomaly_score": -1.023762815, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 345.0, "min_metric_value": 317.760387382, "max_metric_value": 400.46183484, "training_avg": 359.111111111, "training_stddev": 13.783574576, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_count value is 345. The average for this metric is 359.111.", "is_anomalous": false}, {"value": 365.0, "average": 359.421052632, "min_value": 319.031502133, "max_value": 399.81060313, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "6028e5c3ab1e05984de8fd8034252983", "metric_id": "d605223b58deb1c115b6e8df210aaa23", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_count", "anomaly_score": 0.4143854512, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 365.0, "min_metric_value": 319.031502133, "max_metric_value": 399.81060313, "training_avg": 359.421052632, "training_stddev": 13.4631835, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_count value is 365. The average for this metric is 359.421.", "is_anomalous": false}, {"value": 329.0, "average": 357.9, "min_value": 313.60660023, "max_value": 402.19339977, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "5f0887a87c5d531fca00996d9300791e", "metric_id": "a46ae67cc2cc2522459c0c06d84b41bc", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_count", "anomaly_score": -1.957402242, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 329.0, "min_metric_value": 313.60660023, "max_metric_value": 402.19339977, "training_avg": 357.9, "training_stddev": 14.76446659, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_count value is 329. The average for this metric is 357.9.", "is_anomalous": false}, {"value": 374.0, "average": 358.666666667, "min_value": 314.226819682, "max_value": 403.106513651, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "456c3cb8e645054dfa2aa17d243a3c78", "metric_id": "a735659e4ff0da4a9f24cfe5f4e04441", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_count", "anomaly_score": 1.035107074, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 374.0, "min_metric_value": 314.226819682, "max_metric_value": 403.106513651, "training_avg": 358.666666667, "training_stddev": 14.813282328, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_count value is 374. The average for this metric is 358.667.", "is_anomalous": false}, {"value": 347.0, "average": 358.136363636, "min_value": 314.130239505, "max_value": 402.142487767, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "b444373e2875ebedd9cb770016ff3332", "metric_id": "548895f05466c8d1ce939c5e680428b4", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_count", "anomaly_score": -0.7591918527, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 347.0, "min_metric_value": 314.130239505, "max_metric_value": 402.142487767, "training_avg": 358.136363636, "training_stddev": 14.668708044, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_count value is 347. The average for this metric is 358.136.", "is_anomalous": false}, {"value": 344.0, "average": 357.52173913, "min_value": 313.627417602, "max_value": 401.416060659, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "fb3740346d9a727704118921db74751c", "metric_id": "a36be92954e7cb6d12a11573e2e67119", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_count", "anomaly_score": -0.9241563824, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 344.0, "min_metric_value": 313.627417602, "max_metric_value": 401.416060659, "training_avg": 357.52173913, "training_stddev": 14.63144051, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_count value is 344. The average for this metric is 357.522.", "is_anomalous": false}, {"value": 344.0, "average": 356.958333333, "min_value": 313.237566703, "max_value": 400.679099964, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "0bce79791d03f821bb55ae0acc7c5737", "metric_id": "e28ada4c5d5f7bbd88f046510339a00b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_count", "anomaly_score": -0.8891655613, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 344.0, "min_metric_value": 313.237566703, "max_metric_value": 400.679099964, "training_avg": 356.958333333, "training_stddev": 14.573588877, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_count value is 344. The average for this metric is 356.958.", "is_anomalous": false}, {"value": 364.0, "average": 357.24, "min_value": 314.231744978, "max_value": 400.248255022, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "94667a5da6f6ba09c05bf3f6d64ccbc4", "metric_id": "3d39a4c1087c85372835ef7a9f000064", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_count", "anomaly_score": 0.4715373825, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 364.0, "min_metric_value": 314.231744978, "max_metric_value": 400.248255022, "training_avg": 357.24, "training_stddev": 14.336085007, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_count value is 364. The average for this metric is 357.24.", "is_anomalous": false}, {"value": 350.0, "average": 356.961538462, "min_value": 314.60748135, "max_value": 399.315595574, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "75e3ed2fa0441f7c75128c784f6bc2c5", "metric_id": "4c1ca3ea3a32d8ba24a3ebcdb7feaf0d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_count", "anomaly_score": -0.493095982, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 350.0, "min_metric_value": 314.60748135, "max_metric_value": 399.315595574, "training_avg": 356.961538462, "training_stddev": 14.118019037, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_count value is 350. The average for this metric is 356.962.", "is_anomalous": false}, {"value": 374.0, "average": 357.592592593, "min_value": 314.911905794, "max_value": 400.273279392, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "b516389ab7ee498201edf598a6c3dce2", "metric_id": "07d91b5b28e81ef91a527984ab101813", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_count", "anomaly_score": 1.153266873, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 374.0, "min_metric_value": 314.911905794, "max_metric_value": 400.273279392, "training_avg": 357.592592593, "training_stddev": 14.2268956, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_count value is 374. The average for this metric is 357.593.", "is_anomalous": false}, {"value": 361.0, "average": 357.714285714, "min_value": 315.786910863, "max_value": 399.641660565, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "fd1fb120702ceeed77fbc916cb08e670", "metric_id": "3b0cee2a498ca9e2153efaf2980074ee", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_count", "anomaly_score": 0.2351004062, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 361.0, "min_metric_value": 315.786910863, "max_metric_value": 399.641660565, "training_avg": 357.714285714, "training_stddev": 13.975791617, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_count value is 361. The average for this metric is 357.714.", "is_anomalous": false}, {"value": 55.0, "average": 347.275862069, "min_value": 315.786910863, "max_value": 399.641660565, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "f7b2d079cb1885b10c6c20d85d9909df", "metric_id": "d988f5bab25319a498fcd52d07544808", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_count", "anomaly_score": -5.051109772, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 55.0, "min_metric_value": 173.684784911, "max_metric_value": 520.866939227, "training_avg": 347.275862069, "training_stddev": 57.863692386, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_count value is 55. The average for this metric is 347.276.", "is_anomalous": true}, {"value": 204.0, "average": 342.5, "min_value": 154.741764036, "max_value": 530.258235964, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "ea0aa3a50488523209a2b18006de1e95", "metric_id": "bfafb58950cb0c4f54f4791fc84ff561", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_count", "anomaly_score": -2.212952193, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 204.0, "min_metric_value": 154.741764036, "max_metric_value": 530.258235964, "training_avg": 342.5, "training_stddev": 62.586078655, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_count value is 204. The average for this metric is 342.5.", "is_anomalous": false}], "result_description": "In column NULL_PERCENT_INT, the last null_count value is 204. The average for this metric is 342.5."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "min", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('min' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_INT, the last min value is 103. The average for this metric is 100.1.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('min' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Min", "metrics": [{"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "86775b17adb492b1e1d39a9de7635f23", "metric_id": "8bbce64db1564a725072db9ca482b615", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "592bba1a5acf2093529f0e94dfc506dc", "metric_id": "d8c025f22497f4121205ea16984e1521", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "e0198f81cdff16f509402658bad3a0da", "metric_id": "2d9dae211e8c611953a1c09e03040b6b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "06e920533cd20024b11a32da29da865d", "metric_id": "bcc5f39d68feeaab2e9e24248d32f30a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "b0caa3a5e8ef40b2a31068c58a71144a", "metric_id": "5e475f03456cb06926de8855991a3b5c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "7375d40456e6af5641572ae584995984", "metric_id": "111a96c35f7d8e2ad98492354a2ab6e3", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "7e63d3031b895a4ad5eee42a567d8cee", "metric_id": "e4fe364e28b06f544b7547c2269dd8ac", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "2f71d2e91429a5c84b04d3121ecbd863", "metric_id": "34c8e3463150b7cb5361a4fbb7018dd0", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "ee3d1740b677c200e7c8be201e1b3d49", "metric_id": "45c2cab7ba3bcc75da0a5a5cf38e6ae2", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "e101ebe79c8d31aeac02cf9ea2a4bd59", "metric_id": "fe04e371860445ff7793f7ac222aaf15", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "e587189f274c80907f8468fd88ef1393", "metric_id": "36fd5ac01fe71779171253c609c40ec1", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "166db5cd593b0f525e8bd4e2b5390e37", "metric_id": "c9c544597ebb149e32c700793f7fc9eb", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "a0815a9adbc0e91bf55df5a85a6083c6", "metric_id": "20beaec00dec47201f5570e7802727d4", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "ea9d42bede2b23ab5e344195066f604f", "metric_id": "4b75966401a6ebe8d170c005452254c7", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "52fd0d37c0e753c76970d0a429888950", "metric_id": "35ee2aae72a27d3267c1dc60c8d3b2f0", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "defaaa62b3a86b49c31d1e4a76feb7c9", "metric_id": "1d8089bc33050473de15b0ee95fdc3ef", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "9d3930ad9f03ce3fdaac19434d2fd543", "metric_id": "b9fa5b7fc8d5b252a873c94574ca9406", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "f67afbc090146730e1bee62428cab915", "metric_id": "6bde1e046be0e320ea18f22af7efb8b3", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "b851a5c440429886c850ecb05062e4fe", "metric_id": "f3f15e8ab06075b04b6a47370d8afdc5", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "8abeb21ce7e93769606a72f1ab4163cb", "metric_id": "c3b1250e48d9dce418633c56ff752eea", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "e45338aa6cafdc91d1ad96772a796bb5", "metric_id": "e88489e76346bc63c5d0ff08f74a6f08", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "295e792b854ab0106e5c339ef57ffef8", "metric_id": "ff95a6ebd34003e907e88e3b91605129", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "1958dbca070710d818de34fb743637b1", "metric_id": "050cd6d489e2ef121becb13f59948acc", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "d68d6cfc01c7ba6fc4c7167c7417123a", "metric_id": "764da1f85724a4907f6996a27e76bae3", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "fb7ba24dff06460639cc853f77ec053c", "metric_id": "2321f0005346035556b5360d9f471a3e", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "e743a97f272e7449ca3bbee336778103", "metric_id": "7c0ea214474b1a8d47ca973b12b26012", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "a9dfbc1cf04546a8560ae6b95ab1fe29", "metric_id": "6b279bc0f452f26a60def886ea36ed64", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "3729e0bf9a049cf363dc314829198825", "metric_id": "524258d647cc3af7abfde459a6dfdadb", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 103.0, "average": 100.1, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "47866eec4bb0b4b2bd9e0a32f940f818", "metric_id": "534ce198f5392b998c29b26f4d4edf90", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "min", "anomaly_score": 5.294651389, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 103.0, "min_metric_value": 98.456832327, "max_metric_value": 101.743167673, "training_avg": 100.1, "training_stddev": 0.5477225575, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last min value is 103. The average for this metric is 100.1.", "is_anomalous": true}], "result_description": "In column NULL_COUNT_INT, the last min value is 103. The average for this metric is 100.1."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "min", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('min' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_FLOAT, the last min value is 1.201. The average for this metric is 1.207.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('min' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Min", "metrics": [{"value": 1.204622512, "average": 1.202634822, "min_value": 1.19420177, "max_value": 1.211067874, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "293469d58c7fc6875cc847a4b8eb208f", "metric_id": "f602e33076b8bb03a6bc1226c5952e8a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "min", "anomaly_score": 0.7071067812, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 1.204622512, "min_metric_value": 1.19420177, "max_metric_value": 1.211067874, "training_avg": 1.202634822, "training_stddev": 0.002811017412, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last min value is 1.205. The average for this metric is 1.203.", "is_anomalous": false}, {"value": 1.220502971, "average": 1.208590872, "min_value": 1.177073092, "max_value": 1.240108651, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "160caeebc032ed0bb4d1c4e6f273d4b1", "metric_id": "357e958cd6778472bae054b66ee421d4", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "min", "anomaly_score": 1.133845672, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 1.220502971, "min_metric_value": 1.177073092, "max_metric_value": 1.240108651, "training_avg": 1.208590872, "training_stddev": 0.01050592654, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last min value is 1.221. The average for this metric is 1.209.", "is_anomalous": false}, {"value": 1.200595178, "average": 1.206591948, "min_value": 1.17820019, "max_value": 1.234983707, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "f065700d098fcdce3d9e2802a232e06f", "metric_id": "2209f9475801aed3c75f3d9d134f48a4", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "min", "anomaly_score": -0.6336455136, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 1.200595178, "min_metric_value": 1.17820019, "max_metric_value": 1.234983707, "training_avg": 1.206591948, "training_stddev": 0.00946391959, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last min value is 1.201. The average for this metric is 1.207.", "is_anomalous": false}, {"value": 1.205971282, "average": 1.206467815, "min_value": 1.181865734, "max_value": 1.231069896, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "6278da9c82548b301bb5acf47f44c5f1", "metric_id": "c0d716ece747cdec7e6764b180baa8ef", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "min", "anomaly_score": -0.06054769125, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 1.205971282, "min_metric_value": 1.181865734, "max_metric_value": 1.231069896, "training_avg": 1.206467815, "training_stddev": 0.00820069362, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last min value is 1.206. The average for this metric is 1.206.", "is_anomalous": false}, {"value": 1.205362963, "average": 1.206283673, "min_value": 1.184237337, "max_value": 1.22833001, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "ae6984e86c91b554cf9dd4a4407d7fdf", "metric_id": "6f7433972cbe520506b10dc94b7dcdfb", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "min", "anomaly_score": -0.1252875026, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 1.205362963, "min_metric_value": 1.184237337, "max_metric_value": 1.22833001, "training_avg": 1.206283673, "training_stddev": 0.007348778837, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last min value is 1.205. The average for this metric is 1.206.", "is_anomalous": false}, {"value": 1.212076585, "average": 1.207111232, "min_value": 1.185940972, "max_value": 1.228281492, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "f2ac93a556b3608a0c0874a52db0c2be", "metric_id": "c53a5cac33fd5e9b30b96ac5dc429adb", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "min", "anomaly_score": 0.7036313419, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 1.212076585, "min_metric_value": 1.185940972, "max_metric_value": 1.228281492, "training_avg": 1.207111232, "training_stddev": 0.007056753286, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last min value is 1.212. The average for this metric is 1.207.", "is_anomalous": false}, {"value": 1.201834807, "average": 1.206451679, "min_value": 1.186068476, "max_value": 1.226834881, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "8bf524120a4f64a755cdd0c615e572e8", "metric_id": "29b2ca3f6e607d7a3cdebbdbfc38574a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "min", "anomaly_score": -0.6795112778, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 1.201834807, "min_metric_value": 1.186068476, "max_metric_value": 1.226834881, "training_avg": 1.206451679, "training_stddev": 0.006794400859, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last min value is 1.202. The average for this metric is 1.206.", "is_anomalous": false}, {"value": 1.200483139, "average": 1.205788508, "min_value": 1.185809418, "max_value": 1.225767598, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "c58b179c560e2ba331a26034678b0d9c", "metric_id": "91481ffe7826a837eb452633602ec1d2", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "min", "anomaly_score": -0.7966381283, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 1.200483139, "min_metric_value": 1.185809418, "max_metric_value": 1.225767598, "training_avg": 1.205788508, "training_stddev": 0.006659696697, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last min value is 1.2. The average for this metric is 1.206.", "is_anomalous": false}, {"value": 1.202341532, "average": 1.20544381, "min_value": 1.1863256, "max_value": 1.22456202, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "2ad3933c82993ac91f313e4665985938", "metric_id": "450f9196e2d5c68126fe874925cd07ca", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "min", "anomaly_score": -0.4868047452, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 1.202341532, "min_metric_value": 1.1863256, "max_metric_value": 1.22456202, "training_avg": 1.20544381, "training_stddev": 0.006372736727, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last min value is 1.202. The average for this metric is 1.205.", "is_anomalous": false}, {"value": 1.207258136, "average": 1.205608749, "min_value": 1.187397526, "max_value": 1.223819972, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "1e3eb3e2291eb73b193c28f600744323", "metric_id": "0a9dabed2a8029abd5fcd3e7be0894ca", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "min", "anomaly_score": 0.2717094583, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 1.207258136, "min_metric_value": 1.187397526, "max_metric_value": 1.223819972, "training_avg": 1.205608749, "training_stddev": 0.006070407617, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last min value is 1.207. The average for this metric is 1.206.", "is_anomalous": false}, {"value": 1.206491085, "average": 1.205682277, "min_value": 1.188301752, "max_value": 1.223062802, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "aaaabc46769694074043d11ae596a651", "metric_id": "cc6b2935473cdefffb1fd61bfbcf8664", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "min", "anomaly_score": 0.1396059803, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 1.206491085, "min_metric_value": 1.188301752, "max_metric_value": 1.223062802, "training_avg": 1.205682277, "training_stddev": 0.005793508338, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last min value is 1.206. The average for this metric is 1.206.", "is_anomalous": false}, {"value": 1.203556415, "average": 1.205518749, "min_value": 1.188784418, "max_value": 1.22225308, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "967b8730b3551014b7b37efa6745838c", "metric_id": "0b9bebf57a19144494b142216c97f5c3", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "min", "anomaly_score": -0.351791846, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 1.203556415, "min_metric_value": 1.188784418, "max_metric_value": 1.22225308, "training_avg": 1.205518749, "training_stddev": 0.005578110323, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last min value is 1.204. The average for this metric is 1.206.", "is_anomalous": false}, {"value": 1.201853834, "average": 1.205256969, "min_value": 1.188912825, "max_value": 1.221601114, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "a33d7cf11e899e52e13a0a6619d2ee7f", "metric_id": "badd2d6f0a85f3b08bc046ef7c3948bc", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "min", "anomaly_score": -0.6246522329, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 1.201853834, "min_metric_value": 1.188912825, "max_metric_value": 1.221601114, "training_avg": 1.205256969, "training_stddev": 0.005448048203, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last min value is 1.202. The average for this metric is 1.205.", "is_anomalous": false}, {"value": 1.220718176, "average": 1.206287716, "min_value": 1.186501872, "max_value": 1.22607356, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "a3c3353746195b707cdf49e5591011e5", "metric_id": "20e5223aeaa0c764c60f44e27a593490", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "min", "anomaly_score": 2.187997598, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 1.220718176, "min_metric_value": 1.186501872, "max_metric_value": 1.22607356, "training_avg": 1.206287716, "training_stddev": 0.006595281336, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last min value is 1.221. The average for this metric is 1.206.", "is_anomalous": false}, {"value": 1.207778584, "average": 1.206380896, "min_value": 1.187233279, "max_value": 1.225528513, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "9b1c22ea382042fbd00218db25f41983", "metric_id": "92a32030e88a84da2b39bb88193bb8cb", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "min", "anomaly_score": 0.2189862582, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 1.207778584, "min_metric_value": 1.187233279, "max_metric_value": 1.225528513, "training_avg": 1.206380896, "training_stddev": 0.00638253905, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last min value is 1.208. The average for this metric is 1.206.", "is_anomalous": false}, {"value": 1.203300769, "average": 1.206199712, "min_value": 1.187525146, "max_value": 1.224874278, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "8826614559abd78628bff63c746d9160", "metric_id": "97efc3d012751b65649b22bab1b5a0dc", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "min", "anomaly_score": -0.4657044227, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 1.203300769, "min_metric_value": 1.187525146, "max_metric_value": 1.224874278, "training_avg": 1.206199712, "training_stddev": 0.006224855326, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last min value is 1.203. The average for this metric is 1.206.", "is_anomalous": false}, {"value": 1.203120206, "average": 1.206028628, "min_value": 1.187781244, "max_value": 1.224276012, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "324e045c68b061d65e3412ea8181f1c2", "metric_id": "c22bef25c28a7cd5ff92781b33caba72", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "min", "anomaly_score": -0.4781653862, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 1.203120206, "min_metric_value": 1.187781244, "max_metric_value": 1.224276012, "training_avg": 1.206028628, "training_stddev": 0.006082461217, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last min value is 1.203. The average for this metric is 1.206.", "is_anomalous": false}, {"value": 1.203830594, "average": 1.205912942, "min_value": 1.188115263, "max_value": 1.223710621, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "b300e790ce701148c2c8d7501d28b92d", "metric_id": "dd19645566c215a41d65bf1e1172ae33", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "min", "anomaly_score": -0.3510032585, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 1.203830594, "min_metric_value": 1.188115263, "max_metric_value": 1.223710621, "training_avg": 1.205912942, "training_stddev": 0.005932559671, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last min value is 1.204. The average for this metric is 1.206.", "is_anomalous": false}, {"value": 1.200012141, "average": 1.205617902, "min_value": 1.187848414, "max_value": 1.22338739, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "7fe7fe1d28304a6ce90f8872ffcc3dcf", "metric_id": "44585b16622f6a63f4862cb0f3fabe7a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "min", "anomaly_score": -0.9464135807, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 1.200012141, "min_metric_value": 1.187848414, "max_metric_value": 1.22338739, "training_avg": 1.205617902, "training_stddev": 0.005923162574, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last min value is 1.2. The average for this metric is 1.206.", "is_anomalous": false}, {"value": 1.203442777, "average": 1.205514325, "min_value": 1.188136333, "max_value": 1.222892317, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "fd1a49648ec5f19ffbb01715c263cab3", "metric_id": "eaf2039a05b2bffb0c54e0a49f0abb03", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "min", "anomaly_score": -0.3576156792, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 1.203442777, "min_metric_value": 1.188136333, "max_metric_value": 1.222892317, "training_avg": 1.205514325, "training_stddev": 0.005792663969, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last min value is 1.203. The average for this metric is 1.206.", "is_anomalous": false}, {"value": 1.213904023, "average": 1.205895675, "min_value": 1.188107797, "max_value": 1.223683553, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "843100f0f89634863bbc6395f1c057ce", "metric_id": "1a6146e9ff7064568a089d9677e2c260", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "min", "anomaly_score": 1.350641404, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 1.213904023, "min_metric_value": 1.188107797, "max_metric_value": 1.223683553, "training_avg": 1.205895675, "training_stddev": 0.005929292659, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last min value is 1.214. The average for this metric is 1.206.", "is_anomalous": false}, {"value": 1.208907209, "average": 1.206026611, "min_value": 1.1885459, "max_value": 1.223507322, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "a4647311350142f351d64240aef73761", "metric_id": "da48f4d710fd9806bde5eb3a9a9f1119", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "min", "anomaly_score": 0.4943616819, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 1.208907209, "min_metric_value": 1.1885459, "max_metric_value": 1.223507322, "training_avg": 1.206026611, "training_stddev": 0.005826903745, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last min value is 1.209. The average for this metric is 1.206.", "is_anomalous": false}, {"value": 1.203155636, "average": 1.205906987, "min_value": 1.188720355, "max_value": 1.223093619, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "83088fab01aaf236514a414d4d2a4084", "metric_id": "4a707c9a5a5564d7b7987bc32387a0ed", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "min", "anomaly_score": -0.4802600762, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 1.203155636, "min_metric_value": 1.188720355, "max_metric_value": 1.223093619, "training_avg": 1.205906987, "training_stddev": 0.005728877345, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last min value is 1.203. The average for this metric is 1.206.", "is_anomalous": false}, {"value": 1.200676532, "average": 1.205697769, "min_value": 1.188582817, "max_value": 1.22281272, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "e6d4a93f2f39b7763aff24c742fdc421", "metric_id": "4ff9d55289aa8799cbfc7ee076833c30", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "min", "anomaly_score": -0.8801491804, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 1.200676532, "min_metric_value": 1.188582817, "max_metric_value": 1.22281272, "training_avg": 1.205697769, "training_stddev": 0.005704983835, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last min value is 1.201. The average for this metric is 1.206.", "is_anomalous": false}, {"value": 1.203493585, "average": 1.205612992, "min_value": 1.188793763, "max_value": 1.222432221, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "454ad5b9efb0ab9d8b632a7c90cc9be2", "metric_id": "c36a4f0da3a2e940539fc1086cefefe7", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "min", "anomaly_score": -0.3780329699, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 1.203493585, "min_metric_value": 1.188793763, "max_metric_value": 1.222432221, "training_avg": 1.205612992, "training_stddev": 0.005606409681, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last min value is 1.203. The average for this metric is 1.206.", "is_anomalous": false}, {"value": 1.207900176, "average": 1.205697703, "min_value": 1.189152312, "max_value": 1.222243094, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "e827ca252025250ceb7f1432b965207a", "metric_id": "31df585c2ec1707dfe05ccba87ca50b3", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "min", "anomaly_score": 0.3993510617, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 1.207900176, "min_metric_value": 1.189152312, "max_metric_value": 1.222243094, "training_avg": 1.205697703, "training_stddev": 0.005515130232, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last min value is 1.208. The average for this metric is 1.206.", "is_anomalous": false}, {"value": 1.20205859, "average": 1.205567735, "min_value": 1.189201067, "max_value": 1.221934402, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "cd0470a76bd415224fe8c7a10664967c", "metric_id": "47b4282b7eedc59eb583d0678fb88372", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "min", "anomaly_score": -0.6432240851, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 1.20205859, "min_metric_value": 1.189201067, "max_metric_value": 1.221934402, "training_avg": 1.205567735, "training_stddev": 0.005455555691, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last min value is 1.202. The average for this metric is 1.206.", "is_anomalous": false}, {"value": 1.26113903, "average": 1.207483986, "min_value": 1.189201067, "max_value": 1.221934402, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "84fa1df8221b0c05190e9444282ca1ac", "metric_id": "3878a609cea6e5baaeb8e9efe95d3757", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "min", "anomaly_score": 4.614666868, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 1.26113903, "min_metric_value": 1.172602783, "max_metric_value": 1.242365189, "training_avg": 1.207483986, "training_stddev": 0.0116270677, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last min value is 1.261. The average for this metric is 1.207.", "is_anomalous": true}, {"value": 1.201323963, "average": 1.207278652, "min_value": 1.172838458, "max_value": 1.241718846, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "ec754167d469f239a80e7ff460fbaf14", "metric_id": "234a6e1383b1535aeb76fe43712f934a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "min", "anomaly_score": -0.5186981967, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 1.201323963, "min_metric_value": 1.172838458, "max_metric_value": 1.241718846, "training_avg": 1.207278652, "training_stddev": 0.01148006475, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last min value is 1.201. The average for this metric is 1.207.", "is_anomalous": false}], "result_description": "In column NULL_PERCENT_FLOAT, the last min value is 1.201. The average for this metric is 1.207."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "missing_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('missing_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_STR, the last missing_percent value is 80. The average for this metric is 5.567.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('missing_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Missing Percent", "metrics": [{"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "9c6d97b9c5c3fc78c11cd1f8a6bee8e3", "metric_id": "a06a04a6a92b97a650c882ccf1964deb", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "92854ef2c3b8f7dd0a6514d3fec4a153", "metric_id": "8732e7695988fe2016f6f18462d70ec6", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "1e1d2686e0eda8faf68a544dcc090873", "metric_id": "dd823669a254e55867d5338bab146923", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "4469d33ce30216909cfdd6bc65e5f885", "metric_id": "41ff00ad18e1f9a80ab6b0f37b15fc9a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "8960e5b618ddc07bfae4ed8752a0453c", "metric_id": "f7c0e9cc9a734e74da6acff10b17c457", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "10694fcbc7508b02b9a16b933a2e2062", "metric_id": "a4725c65193b35e0a9495910a084c3e7", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "ffd6c605855ea9e9659bfc6108d9e7c6", "metric_id": "a3390ecbf83ed497821f90be4e7aeeb0", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "f2331b5d11c347ae3acb2ad30f4379a9", "metric_id": "f7866256508e928fcab9e2c120a9fa0c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "e1b27ed51b60651cc92aa8ccc1fa4124", "metric_id": "87a733326a03e45fc2c3fdddee9af010", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "24c0e11c5601ed0c30fe0cc56e6edc9b", "metric_id": "8120d16b062130383f6e2fad397faa80", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "f50aa3537e3ec7c30f658ae3fb07d0fe", "metric_id": "34d569754329a1f7973f65e0715f6fdb", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "571608c2b26e02f4b59dab9038eef601", "metric_id": "f0c558d0abf40d6cb3ea6d502990822d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "8adc340c0ba24681338965bbc60b60c7", "metric_id": "4147c3d4e564e60f1cc56d27bfe9a774", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "39c7ca01a7d7c990dae458f829daedab", "metric_id": "ced206727c9a7843e8534c2fc2ad6347", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "fa0a8de2e7e9b6a1b6145a2803443379", "metric_id": "246d878264f6f87a2222135b08b90a72", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "9585c9713026e16abb57610b7c0eeb20", "metric_id": "c2423f5152190c702bd61d376a3783a8", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "13b58f7072fa0f5d0b457040e0e0582b", "metric_id": "06d77b6de8301c6d19a3da8c57dbcfea", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "24223705eaa51a09635668614375f535", "metric_id": "588ff1861151a67d99e56c14dd1cb812", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "51662f6f18c829dec240193686f2bbeb", "metric_id": "8329f456b844477ab4a864b16aeb22af", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "ce5841849f8a0ff1f7b3580491371f59", "metric_id": "e14b4f9a15b2fd86e4fa6f613657a2d4", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "3801ed76c3e72aad279d1f2c5a864359", "metric_id": "c7c87bd54a5d1d5f6a7a70a6c5efc4a2", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "58408d9d0c5eb226ae625d2e9fdc9047", "metric_id": "6536602ab93ba88bc45ba9dd5fb1525e", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "3232894e05e6d5e0289fdfcb0aefa4b4", "metric_id": "a9f3cf75027ef2b26d5eb9e09b77d7dc", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "6d0f8cd1a9506914100857c6d4abca83", "metric_id": "87846c7e7e6d8644b61e2a8c4091fe76", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "9f4f633532204145bbd3b90bd6b9c1b1", "metric_id": "f2046d96e132d0decf167edac40ff52d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "557ac02d137be17f76ef595255df83c5", "metric_id": "67f7181fe5c2895c3a78fa3ce04f9151", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "c5e2226c7dfe56c6cab9fe651ced5037", "metric_id": "8be71e2c5b4290b2a63f2dba35a6c28d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "9816c17cb95f0d09938f3c1d03e4dfaf", "metric_id": "19b07f12c0099b35b1e16c9c254596da", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 80.0, "average": 5.566666667, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "1eb46b1fd33644626a63a876ee8bf0df", "metric_id": "1e14d5672ec7489d3c603d48663b252a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_percent", "anomaly_score": 5.294651389, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 80.0, "min_metric_value": -36.607970261, "max_metric_value": 47.741303595, "training_avg": 5.566666667, "training_stddev": 14.058212309, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_percent value is 80. The average for this metric is 5.567.", "is_anomalous": true}], "result_description": "In column NULL_COUNT_STR, the last missing_percent value is 80. The average for this metric is 5.567."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_BOOL", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_BOOL' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_BOOL, the last null_count value is 175. The average for this metric is 339.533.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_BOOL' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Null Count", "metrics": [{"value": 327.0, "average": 351.0, "min_value": 249.176623509, "max_value": 452.823376491, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "7b0e707bbb793a0f092036b4693d1a6a", "metric_id": "12a06f52e6c814a1933f7bcbbab352aa", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_count", "anomaly_score": -0.7071067812, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 327.0, "min_metric_value": 249.176623509, "max_metric_value": 452.823376491, "training_avg": 351.0, "training_stddev": 33.941125497, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_count value is 327. The average for this metric is 351.", "is_anomalous": false}, {"value": 373.0, "average": 358.333333333, "min_value": 276.871685916, "max_value": 439.794980751, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "430e4a0316b1272b3dd6e3fb1401f2dc", "metric_id": "4b9da3fca333a07188aa6e0d498db33c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_count", "anomaly_score": 0.5401314778, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 373.0, "min_metric_value": 276.871685916, "max_metric_value": 439.794980751, "training_avg": 358.333333333, "training_stddev": 27.153882473, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_count value is 373. The average for this metric is 358.333.", "is_anomalous": false}, {"value": 376.0, "average": 362.75, "min_value": 291.152164837, "max_value": 434.347835163, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "e05642d0db1efe40164dd472b36dc884", "metric_id": "1b7b2b0a0a543249ac6a272ed2514914", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_count", "anomaly_score": 0.5551843839, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 376.0, "min_metric_value": 291.152164837, "max_metric_value": 434.347835163, "training_avg": 362.75, "training_stddev": 23.865945054, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_count value is 376. The average for this metric is 362.75.", "is_anomalous": false}, {"value": 386.0, "average": 367.4, "min_value": 297.990346493, "max_value": 436.809653507, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "821b2da402aa88ba5b78270a5db03912", "metric_id": "f827afeb8f8fdc47399ebe724717312d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_count", "anomaly_score": 0.8039227569, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 386.0, "min_metric_value": 297.990346493, "max_metric_value": 436.809653507, "training_avg": 367.4, "training_stddev": 23.136551169, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_count value is 386. The average for this metric is 367.4.", "is_anomalous": false}, {"value": 359.0, "average": 366.0, "min_value": 303.07146911, "max_value": 428.92853089, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "335a8508ca5d260800a5ed218ca4acde", "metric_id": "93bd386fbaf2dc48e640a77448a0f363", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_count", "anomaly_score": -0.3337119062, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 359.0, "min_metric_value": 303.07146911, "max_metric_value": 428.92853089, "training_avg": 366.0, "training_stddev": 20.976176963, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_count value is 359. The average for this metric is 366.", "is_anomalous": false}, {"value": 341.0, "average": 362.428571429, "min_value": 298.369446953, "max_value": 426.487695904, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "d13c29060309473ce28c42c0fb914a2c", "metric_id": "aa017c847cb0cd82cd1a9788122e95f8", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_count", "anomaly_score": -1.003537198, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 341.0, "min_metric_value": 298.369446953, "max_metric_value": 426.487695904, "training_avg": 362.428571429, "training_stddev": 21.353041492, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_count value is 341. The average for this metric is 362.429.", "is_anomalous": false}, {"value": 348.0, "average": 360.625, "min_value": 299.375072886, "max_value": 421.874927114, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "6c0c4994c93c27c540d819f3aa7834c3", "metric_id": "07d0eab7ff888c88d6aae6773c4a1347", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_count", "anomaly_score": -0.6183680828, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 348.0, "min_metric_value": 299.375072886, "max_metric_value": 421.874927114, "training_avg": 360.625, "training_stddev": 20.416642371, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_count value is 348. The average for this metric is 360.625.", "is_anomalous": false}, {"value": 383.0, "average": 363.111111111, "min_value": 301.602981567, "max_value": 424.619240655, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "bf5b152015416782a9067769b883e61b", "metric_id": "a0dd0743613b2e2c9e7b59cb3848ae64", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_count", "anomaly_score": 0.9700614717, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 383.0, "min_metric_value": 301.602981567, "max_metric_value": 424.619240655, "training_avg": 363.111111111, "training_stddev": 20.502709848, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_count value is 383. The average for this metric is 363.111.", "is_anomalous": false}, {"value": 335.0, "average": 360.3, "min_value": 296.471323059, "max_value": 424.128676941, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "7ba8a529aa974743be1c80c777632faa", "metric_id": "be200f8deaca6deeb23ef5583732ba14", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_count", "anomaly_score": -1.189120684, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 335.0, "min_metric_value": 296.471323059, "max_metric_value": 424.128676941, "training_avg": 360.3, "training_stddev": 21.276225647, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_count value is 335. The average for this metric is 360.3.", "is_anomalous": false}, {"value": 363.0, "average": 360.545454545, "min_value": 299.943024351, "max_value": 421.14788474, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "757cc3fad2835b856efed8bd63ace135", "metric_id": "71741ab5b1647744e1a7b6345d396762", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_count", "anomaly_score": 0.1215072785, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 363.0, "min_metric_value": 299.943024351, "max_metric_value": 421.14788474, "training_avg": 360.545454545, "training_stddev": 20.200810065, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_count value is 363. The average for this metric is 360.545.", "is_anomalous": false}, {"value": 342.0, "average": 359.0, "min_value": 299.027278928, "max_value": 418.972721072, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "143b0c2bc68533edde3c654f3a3f4cde", "metric_id": "87728cce968a1acd519ceb179f120723", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_count", "anomaly_score": -0.8503866273, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 342.0, "min_metric_value": 299.027278928, "max_metric_value": 418.972721072, "training_avg": 359.0, "training_stddev": 19.990907024, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_count value is 342. The average for this metric is 359.", "is_anomalous": false}, {"value": 380.0, "average": 360.615384615, "min_value": 300.596156927, "max_value": 420.634612304, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "1798eb669d1a12391e05c35986f7f84f", "metric_id": "d731dbf3c0235fef7efc530b7c32397c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_count", "anomaly_score": 0.9689202676, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 380.0, "min_metric_value": 300.596156927, "max_metric_value": 420.634612304, "training_avg": 360.615384615, "training_stddev": 20.006409229, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_count value is 380. The average for this metric is 360.615.", "is_anomalous": false}, {"value": 339.0, "average": 359.071428571, "min_value": 298.858756034, "max_value": 419.284101109, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "2038493b82b9a912e237554d1cb56aca", "metric_id": "be614f87e58f04369e23857ecb4a9adb", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_count", "anomaly_score": -1.000026791, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 339.0, "min_metric_value": 298.858756034, "max_metric_value": 419.284101109, "training_avg": 359.071428571, "training_stddev": 20.070890846, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_count value is 339. The average for this metric is 359.071.", "is_anomalous": false}, {"value": 332.0, "average": 357.266666667, "min_value": 295.571332275, "max_value": 418.962001058, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "b62fcd68170e75fff0424cea2ccc00a4", "metric_id": "8f8e2657e9929feda8f23678d5f27481", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_count", "anomaly_score": -1.228618027, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 332.0, "min_metric_value": 295.571332275, "max_metric_value": 418.962001058, "training_avg": 357.266666667, "training_stddev": 20.565111464, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_count value is 332. The average for this metric is 357.267.", "is_anomalous": false}, {"value": 355.0, "average": 357.125, "min_value": 297.497405717, "max_value": 416.752594283, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "beaa3732c9a17d8c322b567cc8182a54", "metric_id": "0cfc2bf5e346ee4979ae984be204d7b3", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_count", "anomaly_score": -0.1069135872, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 355.0, "min_metric_value": 297.497405717, "max_metric_value": 416.752594283, "training_avg": 357.125, "training_stddev": 19.875864761, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_count value is 355. The average for this metric is 357.125.", "is_anomalous": false}, {"value": 354.0, "average": 356.941176471, "min_value": 299.162249414, "max_value": 414.720103527, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "6d58fd44c3c5fc4441c62b3be3c8b0d7", "metric_id": "a24bbd22e4935273312bf6a738d44157", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_count", "anomaly_score": -0.1527118945, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 354.0, "min_metric_value": 299.162249414, "max_metric_value": 414.720103527, "training_avg": 356.941176471, "training_stddev": 19.259642352, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_count value is 354. The average for this metric is 356.941.", "is_anomalous": false}, {"value": 356.0, "average": 356.888888889, "min_value": 300.83114555, "max_value": 412.946632228, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "df9f759ec3e0cd8c91581a9539c1737a", "metric_id": "bf6766a6496334df2d384ca38eed5c5d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_count", "anomaly_score": -0.04756999672, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 356.0, "min_metric_value": 300.83114555, "max_metric_value": 412.946632228, "training_avg": 356.888888889, "training_stddev": 18.685914446, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_count value is 356. The average for this metric is 356.889.", "is_anomalous": false}, {"value": 351.0, "average": 356.578947368, "min_value": 301.950055841, "max_value": 411.207838895, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "0bb7239a7dd6484a0718737ff8b04e2a", "metric_id": "9942156b10e87653cbe36a5e38162415", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_count", "anomaly_score": -0.3063734525, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 351.0, "min_metric_value": 301.950055841, "max_metric_value": 411.207838895, "training_avg": 356.578947368, "training_stddev": 18.209630509, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_count value is 351. The average for this metric is 356.579.", "is_anomalous": false}, {"value": 349.0, "average": 356.2, "min_value": 302.785631738, "max_value": 409.614368262, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "985ee7f3a108dbb4e3367d5a84a5fe99", "metric_id": "9d048a3725cdc95e3cf466e54cc2c28d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_count", "anomaly_score": -0.4043855746, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 349.0, "min_metric_value": 302.785631738, "max_metric_value": 409.614368262, "training_avg": 356.2, "training_stddev": 17.804789421, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_count value is 349. The average for this metric is 356.2.", "is_anomalous": false}, {"value": 360.0, "average": 356.380952381, "min_value": 304.259665257, "max_value": 408.502239505, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "560b3d5dc89b8ecf6d8701aa056df930", "metric_id": "f8754b12d3e2dbc0f3e412372fab97f5", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_count", "anomaly_score": 0.2083053481, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 360.0, "min_metric_value": 304.259665257, "max_metric_value": 408.502239505, "training_avg": 356.380952381, "training_stddev": 17.373762375, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_count value is 360. The average for this metric is 356.381.", "is_anomalous": false}, {"value": 347.0, "average": 355.954545455, "min_value": 304.736714245, "max_value": 407.172376664, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "a360a83f0e3e746d0af1a486ceb5016e", "metric_id": "cf065ee86361d13b4758d5aa6912bc1a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_count", "anomaly_score": -0.5244977331, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 347.0, "min_metric_value": 304.736714245, "max_metric_value": 407.172376664, "training_avg": 355.954545455, "training_stddev": 17.072610403, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_count value is 347. The average for this metric is 355.955.", "is_anomalous": false}, {"value": 322.0, "average": 354.47826087, "min_value": 300.116822258, "max_value": 408.839699481, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "9c4a8b5169bcd9de7f4e9d7e22303635", "metric_id": "ea60afcb22aeef58539148dea1fe8355", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_count", "anomaly_score": -1.792351069, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 322.0, "min_metric_value": 300.116822258, "max_metric_value": 408.839699481, "training_avg": 354.47826087, "training_stddev": 18.120479537, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_count value is 322. The average for this metric is 354.478.", "is_anomalous": false}, {"value": 366.0, "average": 354.958333333, "min_value": 301.325675777, "max_value": 408.590990889, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "be55fd161442576f19d705232cfd7415", "metric_id": "b72340a6d5562ba6717051e5028572fe", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_count", "anomaly_score": 0.6176274216, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 366.0, "min_metric_value": 301.325675777, "max_metric_value": 408.590990889, "training_avg": 354.958333333, "training_stddev": 17.877552519, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_count value is 366. The average for this metric is 354.958.", "is_anomalous": false}, {"value": 385.0, "average": 356.16, "min_value": 300.648649809, "max_value": 411.671350191, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "a4d90bc18da322c7d0bd0c8d9e8bdc31", "metric_id": "0b78e87c4d6d9ea611affe7087ef4a23", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_count", "anomaly_score": 1.558600173, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 385.0, "min_metric_value": 300.648649809, "max_metric_value": 411.671350191, "training_avg": 356.16, "training_stddev": 18.503783397, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_count value is 385. The average for this metric is 356.16.", "is_anomalous": false}, {"value": 352.0, "average": 356.0, "min_value": 301.555165534, "max_value": 410.444834466, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "3cd9f0b85cc625ec5a5fa490cf425d7d", "metric_id": "b5338a1e6ca9f4ba93f543c1f7517bfe", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_count", "anomaly_score": -0.2204065843, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 352.0, "min_metric_value": 301.555165534, "max_metric_value": 410.444834466, "training_avg": 356.0, "training_stddev": 18.148278155, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_count value is 352. The average for this metric is 356.", "is_anomalous": false}, {"value": 345.0, "average": 355.592592593, "min_value": 301.828626011, "max_value": 409.356559174, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "4da27df4b9528acc386e6a318a8d44ac", "metric_id": "78472f59f42a8240646537eb3ac76c0d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_count", "anomaly_score": -0.591060887, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 345.0, "min_metric_value": 301.828626011, "max_metric_value": 409.356559174, "training_avg": 355.592592593, "training_stddev": 17.921322194, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_count value is 345. The average for this metric is 355.593.", "is_anomalous": false}, {"value": 340.0, "average": 355.035714286, "min_value": 301.541277548, "max_value": 408.530151024, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "6bd60bd40dad16912292dd2726148594", "metric_id": "7dfd261a233a7d6103c0b4779bede91b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_count", "anomaly_score": -0.843211848, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 340.0, "min_metric_value": 301.541277548, "max_metric_value": 408.530151024, "training_avg": 355.035714286, "training_stddev": 17.831478913, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_count value is 340. The average for this metric is 355.036.", "is_anomalous": false}, {"value": 70.0, "average": 345.206896552, "min_value": 301.541277548, "max_value": 408.530151024, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "d90fbbdf7f1a8082441d012c42c7bc7b", "metric_id": "9b942c3d23b77f5aaa00a2ba31d14627", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_count", "anomaly_score": -4.936361247, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 70.0, "min_metric_value": 177.954005525, "max_metric_value": 512.459787578, "training_avg": 345.206896552, "training_stddev": 55.750963675, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_count value is 70. The average for this metric is 345.207.", "is_anomalous": true}, {"value": 175.0, "average": 339.533333333, "min_value": 150.588787555, "max_value": 528.477879111, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "3e7b7a0d5decedb6d87c4808a51f8e7b", "metric_id": "893fd19e8eec695eb395793d68653263", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_count", "anomaly_score": -2.612406714, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 175.0, "min_metric_value": 150.588787555, "max_metric_value": 528.477879111, "training_avg": 339.533333333, "training_stddev": 62.981515259, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_count value is 175. The average for this metric is 339.533.", "is_anomalous": false}], "result_description": "In column NULL_PERCENT_BOOL, the last null_count value is 175. The average for this metric is 339.533."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "average_length", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average_length' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_STR, the last average_length value is 5. The average for this metric is 5.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average_length' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Average Length", "metrics": [{"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "8231589615dcfd410f704e9c3ef10966", "metric_id": "24c7989c5b28bbf7ec609d416c100322", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "d1a5a36fa0058ef6264cc6eb9a0d55e8", "metric_id": "fac1884960ac5362656b02e718b106be", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "855d598d53f1c290bd4e84063f97e839", "metric_id": "64247bda5d15cd456e82908f2b643c04", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "5fc31adde4b715a33abfd378cf87ee1a", "metric_id": "bfaa59de7cdbcf48410be399d5c75815", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "e4b4f09c425b66a8b57354e10b8caffb", "metric_id": "4e997d07e07d628917925b4643ce8bf6", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "2fce37ef0f2d2be27c411ac60a8e88c4", "metric_id": "d672155695b39e636013a85b6d1b8f69", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "8403e89db653f5348bd82c13a7037f67", "metric_id": "68ab474cbabf80c1bfd2117ee28fd3ea", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "3b3d2eaa42312d183733f6afd2ef2541", "metric_id": "cb419cc30c494f1fd3b1cb45f4322948", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "41931d4c4dd45618c51572a53444b1e9", "metric_id": "9865b229c2bf4c2d94b2cad48124b779", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "936876c7a239ee68159423dae9518e14", "metric_id": "0bf03ef717430ca2b8176fb2b95ec7a3", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "e4c4a5f543b6fd877c137e4536e555d8", "metric_id": "ecc8082d51adb0c8deb6979173991f24", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "d7d5475f01a0e10dc7575cd202b66fac", "metric_id": "e86693db44d9895494f864eaf100ec73", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "a5445e570ac32d7d1ac9247f68b00e7d", "metric_id": "2d730969adb5df5313f603255f7b2f1c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "41232a57c8b07fce05842fa17a167d86", "metric_id": "ed1fcf910f2cacea1e62f16f780b9026", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "a0d1d3007e7fedb1a4e52622bbf06b4d", "metric_id": "c3d3142b73c80756ea001542c5580d06", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "773f827d2dd719a5d7890ab79f3d3d70", "metric_id": "1cf79b2eb7024f5d23fd6b8fd4affb93", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "0a69b3ec425719ef346d9eebe4317999", "metric_id": "859ca1c4c79e76a60c4db1640fa3c67b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "067b903c131dc1ab83e677754c11ccc2", "metric_id": "af6560c114008ad57f3529ccdac96613", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "c5e0dc2b5730d3a6d04679cd4fdce887", "metric_id": "bcc282e6cd62a2a5abd8edc9c4b7fe63", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "9e641e6194aa0a04ee5a3d52f25cff9c", "metric_id": "27206f529c72914b2e265759b0ed4697", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "717f86b008034383cd23585ae90828d8", "metric_id": "9f868aff6f60c8fc2310b2bd865707c7", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "ecdce2973d5400bb8720b740aa34e407", "metric_id": "da533dc0bac437a8058c89889d5c3a51", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "df953d7be7f464c1e8295b4e3206700e", "metric_id": "8b2e67bc306ffb9527c3246cbd4e33f2", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "8728de406f3c62bf4e4e5b9829fb8b02", "metric_id": "db8778a32b78aa054048dbc12a9d7d3a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "a18014454bc85321e5a95e0fdff92c50", "metric_id": "fd1e3616ebf8571068b6474a00821c97", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "0ef44f897b4569587131c8235182e860", "metric_id": "dca9a76dd6e31aa9df1d92b6e524ef03", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "524ee02f120c84c78ac6ec6cd96e74c0", "metric_id": "384bf878dfc822a514cec4792f3f41b8", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "6d209af4684f140029faaa749f78b92c", "metric_id": "a62be9d3b11f3d74a383e32fd3945745", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "3618b72d05f9a997e443e9986e3a1d92", "metric_id": "a64500a205348c526cc3f2765c382e7d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}], "result_description": "In column NULL_PERCENT_STR, the last average_length value is 5. The average for this metric is 5."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "standard_deviation", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('standard_deviation' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_INT, the last standard_deviation value is 30.033. The average for this metric is 29.182.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('standard_deviation' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Standard Deviation", "metrics": [{"value": 28.747220836, "average": 28.910355857, "min_value": 28.218232581, "max_value": 29.602479132, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "3911c6ea02f1cee6be9650389a3a915b", "metric_id": "70e2d5f11472e74bd577cf25fa9a79bb", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "standard_deviation", "anomaly_score": -0.7071067812, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 28.747220836, "min_metric_value": 28.218232581, "max_metric_value": 29.602479132, "training_avg": 28.910355857, "training_stddev": 0.2307077584, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last standard_deviation value is 28.747. The average for this metric is 28.91.", "is_anomalous": false}, {"value": 28.601599489, "average": 28.807437067, "min_value": 28.082517211, "max_value": 29.532356924, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "035d46581dfb0c60e3606e37cd146de7", "metric_id": "8e379371269ae2d2ebf38e82b068edfb", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "standard_deviation", "anomaly_score": -0.8518358689, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 28.601599489, "min_metric_value": 28.082517211, "max_metric_value": 29.532356924, "training_avg": 28.807437067, "training_stddev": 0.2416399521, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last standard_deviation value is 28.602. The average for this metric is 28.807.", "is_anomalous": false}, {"value": 28.491211292, "average": 28.728380623, "min_value": 27.969871318, "max_value": 29.486889929, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "4e0c994d4bdca5d99f99763e2602b38d", "metric_id": "699cccd6bef7ed7bb6bd13ddf34ce0be", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "standard_deviation", "anomaly_score": -0.9380346298, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 28.491211292, "min_metric_value": 27.969871318, "max_metric_value": 29.486889929, "training_avg": 28.728380623, "training_stddev": 0.2528364352, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last standard_deviation value is 28.491. The average for this metric is 28.728.", "is_anomalous": false}, {"value": 29.521663495, "average": 28.887037198, "min_value": 27.636342126, "max_value": 30.13773227, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "280a15d4c1a93df788be6ab837900fcf", "metric_id": "b2932e24e15d9b3c6acb551f60043366", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "standard_deviation", "anomaly_score": 1.522256652, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 29.521663495, "min_metric_value": 27.636342126, "max_metric_value": 30.13773227, "training_avg": 28.887037198, "training_stddev": 0.4168983572, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last standard_deviation value is 29.522. The average for this metric is 28.887.", "is_anomalous": false}, {"value": 30.202773163, "average": 29.106326525, "min_value": 27.144660849, "max_value": 31.067992202, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "f0941cdd8fb3df12cd0ac89e7d77314c", "metric_id": "be98c06e72a608e15106293ab954cb05", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "standard_deviation", "anomaly_score": 1.676809638, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 30.202773163, "min_metric_value": 27.144660849, "max_metric_value": 31.067992202, "training_avg": 29.106326525, "training_stddev": 0.6538885588, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last standard_deviation value is 30.203. The average for this metric is 29.106.", "is_anomalous": false}, {"value": 28.793548915, "average": 29.06164401, "min_value": 27.236114479, "max_value": 30.887173541, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "1cd33fb7e7a781e24ed90d335941b4ac", "metric_id": "5e808e1202d45d842f73530dbd5c68be", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "standard_deviation", "anomaly_score": -0.4405764299, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 28.793548915, "min_metric_value": 27.236114479, "max_metric_value": 30.887173541, "training_avg": 29.06164401, "training_stddev": 0.6085098437, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last standard_deviation value is 28.794. The average for this metric is 29.062.", "is_anomalous": false}, {"value": 29.254298493, "average": 29.08572582, "min_value": 27.383305906, "max_value": 30.788145734, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "b96868372cc91cfdd9cdb2eb5ac4de0a", "metric_id": "9c0a5a5052717a44952503e056b49dec", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "standard_deviation", "anomaly_score": 0.2970583307, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 29.254298493, "min_metric_value": 27.383305906, "max_metric_value": 30.788145734, "training_avg": 29.08572582, "training_stddev": 0.5674733047, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last standard_deviation value is 29.254. The average for this metric is 29.086.", "is_anomalous": false}, {"value": 28.834730354, "average": 29.057837435, "min_value": 27.44571055, "max_value": 30.66996432, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "a949367a1ec11d9090089740b60b2121", "metric_id": "421d057a1c416712f1eee0461c7f194c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "standard_deviation", "anomaly_score": -0.41517901, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 28.834730354, "min_metric_value": 27.44571055, "max_metric_value": 30.66996432, "training_avg": 29.057837435, "training_stddev": 0.5373756282, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last standard_deviation value is 28.835. The average for this metric is 29.058.", "is_anomalous": false}, {"value": 29.3793181, "average": 29.089985501, "min_value": 27.539761207, "max_value": 30.640209796, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "6fd0d0f21388346298e65553ac132aef", "metric_id": "c30bf44d42930d31d749a897822d391b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "standard_deviation", "anomaly_score": 0.5599175539, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 29.3793181, "min_metric_value": 27.539761207, "max_metric_value": 30.640209796, "training_avg": 29.089985501, "training_stddev": 0.5167414314, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last standard_deviation value is 29.379. The average for this metric is 29.09.", "is_anomalous": false}, {"value": 28.900469016, "average": 29.07275673, "min_value": 27.592127792, "max_value": 30.553385668, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "31627d6c8e4f8fea7e8c1c034c23586b", "metric_id": "f7a907b06c359c93e4308d78f9a9ac41", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "standard_deviation", "anomaly_score": -0.3490835062, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 28.900469016, "min_metric_value": 27.592127792, "max_metric_value": 30.553385668, "training_avg": 29.07275673, "training_stddev": 0.4935429793, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last standard_deviation value is 28.9. The average for this metric is 29.073.", "is_anomalous": false}, {"value": 28.705217349, "average": 29.042128448, "min_value": 27.594965914, "max_value": 30.489290982, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "b59213e883171dbf75de4253eb809d1e", "metric_id": "f913e38352774c94442d8c34a91cfc66", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "standard_deviation", "anomaly_score": -0.6984241743, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 28.705217349, "min_metric_value": 27.594965914, "max_metric_value": 30.489290982, "training_avg": 29.042128448, "training_stddev": 0.4823875113, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last standard_deviation value is 28.705. The average for this metric is 29.042.", "is_anomalous": false}, {"value": 28.843111214, "average": 29.02681943, "min_value": 27.631406609, "max_value": 30.422232251, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "2507d567d3f0053659a0838468ee5081", "metric_id": "7f4091cd479577c444399ae1c32df154", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "standard_deviation", "anomaly_score": -0.3949545533, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 28.843111214, "min_metric_value": 27.631406609, "max_metric_value": 30.422232251, "training_avg": 29.02681943, "training_stddev": 0.4651376071, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last standard_deviation value is 28.843. The average for this metric is 29.027.", "is_anomalous": false}, {"value": 29.216853902, "average": 29.040393321, "min_value": 27.691093627, "max_value": 30.389693015, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "ecd34f3109837e4477c4759327b334ba", "metric_id": "c4d6baf9308e28b44c36461ef6eca504", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "standard_deviation", "anomaly_score": 0.3923381472, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 29.216853902, "min_metric_value": 27.691093627, "max_metric_value": 30.389693015, "training_avg": 29.040393321, "training_stddev": 0.4497665647, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last standard_deviation value is 29.217. The average for this metric is 29.04.", "is_anomalous": false}, {"value": 29.266440601, "average": 29.05546314, "min_value": 27.743508666, "max_value": 30.367417613, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "993e0fd2fb26920c13b8a69d3e316acd", "metric_id": "c571a888b653fde276704af4aec71895", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "standard_deviation", "anomaly_score": 0.4824347152, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 29.266440601, "min_metric_value": 27.743508666, "max_metric_value": 30.367417613, "training_avg": 29.05546314, "training_stddev": 0.4373181578, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last standard_deviation value is 29.266. The average for this metric is 29.055.", "is_anomalous": false}, {"value": 29.004651873, "average": 29.052287436, "min_value": 27.784246233, "max_value": 30.320328638, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "0783747aec846cac746b9d832c63dfff", "metric_id": "dc10193894304f9d9766eb6091176ab0", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "standard_deviation", "anomaly_score": -0.1126987736, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 29.004651873, "min_metric_value": 27.784246233, "max_metric_value": 30.320328638, "training_avg": 29.052287436, "training_stddev": 0.4226804009, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last standard_deviation value is 29.005. The average for this metric is 29.052.", "is_anomalous": false}, {"value": 29.257613543, "average": 29.064365442, "min_value": 27.827533872, "max_value": 30.301197011, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "e226d06509084b1135d24fadac4522a2", "metric_id": "b4a9f0593181b0082c6d940ad09726a8", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "standard_deviation", "anomaly_score": 0.4687334304, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 29.257613543, "min_metric_value": 27.827533872, "max_metric_value": 30.301197011, "training_avg": 29.064365442, "training_stddev": 0.4122771898, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last standard_deviation value is 29.258. The average for this metric is 29.064.", "is_anomalous": false}, {"value": 28.794352206, "average": 29.049364707, "min_value": 27.834366569, "max_value": 30.264362844, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "ebb194f2c7bcf2fd177ba1e16d223c9c", "metric_id": "3c83c44782ff8ae2e33f9cc38874bcd7", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "standard_deviation", "anomaly_score": -0.6296614603, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 28.794352206, "min_metric_value": 27.834366569, "max_metric_value": 30.264362844, "training_avg": 29.049364707, "training_stddev": 0.4049993791, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last standard_deviation value is 28.794. The average for this metric is 29.049.", "is_anomalous": false}, {"value": 29.226858146, "average": 29.058706467, "min_value": 27.871638172, "max_value": 30.245774761, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "bcd91dbeb73998aa2aaaf3a8f69828ea", "metric_id": "e5d842d774ec1da3eada9ad3eb2b3087", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "standard_deviation", "anomaly_score": 0.4249587335, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 29.226858146, "min_metric_value": 27.871638172, "max_metric_value": 30.245774761, "training_avg": 29.058706467, "training_stddev": 0.3956894314, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last standard_deviation value is 29.227. The average for this metric is 29.059.", "is_anomalous": false}, {"value": 29.721927034, "average": 29.091867495, "min_value": 27.853762482, "max_value": 30.329972508, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "6ebc3ae8d46d9282f6275bbec20a2a5a", "metric_id": "28b4d6c615d7cdb80a4681eb0b27fdc8", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "standard_deviation", "anomaly_score": 1.526670677, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 29.721927034, "min_metric_value": 27.853762482, "max_metric_value": 30.329972508, "training_avg": 29.091867495, "training_stddev": 0.412701671, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last standard_deviation value is 29.722. The average for this metric is 29.092.", "is_anomalous": false}, {"value": 29.016436383, "average": 29.088275537, "min_value": 27.880510109, "max_value": 30.296040966, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "bcd7d7c4b1d40ec7551575cdfa6d1a94", "metric_id": "d72c50c0f9a2913c6beff0e12385a2d4", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "standard_deviation", "anomaly_score": -0.1784431456, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 29.016436383, "min_metric_value": 27.880510109, "max_metric_value": 30.296040966, "training_avg": 29.088275537, "training_stddev": 0.4025884762, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last standard_deviation value is 29.016. The average for this metric is 29.088.", "is_anomalous": false}, {"value": 29.36100332, "average": 29.100672255, "min_value": 27.909175752, "max_value": 30.292168757, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "37c1a124d18b33d42186678b9c3b39ed", "metric_id": "d98b2346e4a82a44ae941d30610d9f13", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "standard_deviation", "anomaly_score": 0.655472505, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 29.36100332, "min_metric_value": 27.909175752, "max_metric_value": 30.292168757, "training_avg": 29.100672255, "training_stddev": 0.3971655009, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last standard_deviation value is 29.361. The average for this metric is 29.101.", "is_anomalous": false}, {"value": 29.473535873, "average": 29.116883716, "min_value": 27.929645022, "max_value": 30.30412241, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "0b557062af910314978c813e7c4bb439", "metric_id": "af2b920370f14e50923158ca9fe99c43", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "standard_deviation", "anomaly_score": 0.901214285, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 29.473535873, "min_metric_value": 27.929645022, "max_metric_value": 30.30412241, "training_avg": 29.116883716, "training_stddev": 0.3957462313, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last standard_deviation value is 29.474. The average for this metric is 29.117.", "is_anomalous": false}, {"value": 29.118971488, "average": 29.116970707, "min_value": 27.955827654, "max_value": 30.27811376, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "ddba4d495f8ea00859ce3a401f83b944", "metric_id": "f42f622738bac388553c757f426fe6a5", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "standard_deviation", "anomaly_score": 0.005169339201, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 29.118971488, "min_metric_value": 27.955827654, "max_metric_value": 30.27811376, "training_avg": 29.116970707, "training_stddev": 0.3870476843, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last standard_deviation value is 29.119. The average for this metric is 29.117.", "is_anomalous": false}, {"value": 29.3088353, "average": 29.12464529, "min_value": 27.98213564, "max_value": 30.267154941, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "038a1d4ffbe0e0f931d3d08e947473bd", "metric_id": "14b87214ab2a1eb866239eb2e351fdb5", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "standard_deviation", "anomaly_score": 0.4836458293, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 29.3088353, "min_metric_value": 27.98213564, "max_metric_value": 30.267154941, "training_avg": 29.12464529, "training_stddev": 0.3808365501, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last standard_deviation value is 29.309. The average for this metric is 29.125.", "is_anomalous": false}, {"value": 28.824717249, "average": 29.113109597, "min_value": 27.979860254, "max_value": 30.246358939, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "4ce2a5d14048e16989b41542141d5002", "metric_id": "13f44e5bc8cfefe1bdfe3f69aec3d063", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "standard_deviation", "anomaly_score": -0.7634480863, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 28.824717249, "min_metric_value": 27.979860254, "max_metric_value": 30.246358939, "training_avg": 29.113109597, "training_stddev": 0.3777497808, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last standard_deviation value is 28.825. The average for this metric is 29.113.", "is_anomalous": false}, {"value": 29.327832863, "average": 29.12106231, "min_value": 28.002926192, "max_value": 30.239198428, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "a3d66887ab50e62fac1866cedd168e2c", "metric_id": "ef922cfa7661afa4dc119aeaf9e757c6", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "standard_deviation", "anomaly_score": 0.5547729386, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 29.327832863, "min_metric_value": 28.002926192, "max_metric_value": 30.239198428, "training_avg": 29.12106231, "training_stddev": 0.3727120393, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last standard_deviation value is 29.328. The average for this metric is 29.121.", "is_anomalous": false}, {"value": 28.940856595, "average": 29.114626392, "min_value": 28.012645572, "max_value": 30.216607211, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "a53d3b693322b393e67c831acc025aff", "metric_id": "121b3c171019083fe8dc497d57ba816b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "standard_deviation", "anomaly_score": -0.4730657565, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 28.940856595, "min_metric_value": 28.012645572, "max_metric_value": 30.216607211, "training_avg": 29.114626392, "training_stddev": 0.3673269399, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last standard_deviation value is 28.941. The average for this metric is 29.115.", "is_anomalous": false}, {"value": 30.217658611, "average": 29.152661986, "min_value": 27.908241625, "max_value": 30.397082346, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "a2a8b050fb88b5b30fb9448964de009d", "metric_id": "77c5e131a3f9e48942d755590cc0c6af", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "standard_deviation", "anomaly_score": 2.567452268, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 30.217658611, "min_metric_value": 27.908241625, "max_metric_value": 30.397082346, "training_avg": 29.152661986, "training_stddev": 0.4148067869, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last standard_deviation value is 30.218. The average for this metric is 29.153.", "is_anomalous": false}, {"value": 30.033093954, "average": 29.182009718, "min_value": 27.867577982, "max_value": 30.496441454, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "7eb025e32a94d2b2b81f65377f68cb5e", "metric_id": "cf37e5fc6a794c1ab2c38b16d9783135", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "standard_deviation", "anomaly_score": 1.942476462, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 30.033093954, "min_metric_value": 27.867577982, "max_metric_value": 30.496441454, "training_avg": 29.182009718, "training_stddev": 0.438143912, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last standard_deviation value is 30.033. The average for this metric is 29.182.", "is_anomalous": false}], "result_description": "In column NULL_PERCENT_INT, the last standard_deviation value is 30.033. The average for this metric is 29.182."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "max_length", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('max_length' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_STR, the last max_length value is 5. The average for this metric is 5.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('max_length' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Max Length", "metrics": [{"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "2aafbea74918ddc47e680c676f5881f9", "metric_id": "8f94d702d6f916eba08b6db423d3ac77", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "08c45e44a4bb326d64176c504f36aecc", "metric_id": "d14748cf8b6eabf43ea06e4463de4423", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "2ab7035eae38b53e86d1be276e8c5263", "metric_id": "c238b54143a564b806e6846e91dd884e", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "8bd7f1d337e8834f5bf8dcf7a2296722", "metric_id": "55941b9448bff1a18d10a19e8019e78c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "0035a0fb0b8bab84d5c22e7b19aa4cb8", "metric_id": "7b9ce3ad3f98054072625b4b83f569df", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "282e6a7f038ee7b290245305d70d2964", "metric_id": "ff160fd55726a55fe9bd6f54303b6ff0", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "234bc1d7e638ef8c787d289025af49cc", "metric_id": "b192e5540c98205d78e155501057dc1d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "35dbc7084b5b7cc1a0678ee7bb833764", "metric_id": "3d0af431e0edc1876ec2dbbbe55e1205", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "f4e19b188feaa1c08af5b88d17304001", "metric_id": "d0248f9db976768d4ba1a8a92def8f0d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "bf7a29ff2bafb2ba38122efca95b0273", "metric_id": "348194d5ffc2bf9b82163479ab2b57a5", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "5ba498b40abd225a2cc0785747fdf1ce", "metric_id": "decec3679064af5ac2e27f53f0b3c503", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "c33d6fbd14e3f9e888d68ed9dd4a699f", "metric_id": "358077e89ca9e0ad63194923ade4075f", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "2f91ee970d7a1ba455e8b9b8e11e3079", "metric_id": "01b68e9d6964695fe3337ecbe978a1dc", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "4cdafcdb9f3db3cdc416e1dc5a2747bc", "metric_id": "be678fba0d9981bdf6096820479b19fd", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "b4f2ca81ef0ca8474059860595da95f0", "metric_id": "e5199e09f85308e73234060a842a450b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "89399fe4fefd6e910498b4fdf3fa6363", "metric_id": "6e64fd91524c1521f0dde200a3fcee8d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "30ff6f6ff2e5ab81d35984d787e28f50", "metric_id": "0cc0f62bf1d2630a576f9dd79ec3e56b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "ab513332441f35dfa4584f509c4ace24", "metric_id": "4163e7517a6d4abcc1fa5a70624fbf45", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "b5f4331bfa419f02e17a4cb3593fde5c", "metric_id": "8df7f2136cf553e10fc9db511a618810", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "05f8c47f1b658d4fdcd66d8dfc2a7968", "metric_id": "946794c00e6b0e2fa46ad78389a2433b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "735c9926e392f5733a449005b6c517ea", "metric_id": "043cb5ea6c3b19c77f78c6833a84c6e4", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "1b5ce273a65527af67bac4ca6452ccbb", "metric_id": "e010fe5e47492c6842a687e6cf25d9fb", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "abfea7a318f5dda7c97ba8b64c81530b", "metric_id": "1b617adb350773d72e0ff7b7b726d5a9", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "0db0d0da2baaede8b417af05465aa80a", "metric_id": "45cf11fc28b4234f0ba41c4cac59b1b4", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "e82ceacee449840746ccdc5973bab615", "metric_id": "44f603cf1681d21984e902a3cf874fd3", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "7ef345f0e1c7c5f6e943d78dc7ce73d1", "metric_id": "ea8d9cda070be00ca390157659a30ace", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "115080b006af117544b0d16f3a591b84", "metric_id": "84f3b97536b14d8614ad7de838889914", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "011ddbef4088e58b9343b70204adf4e0", "metric_id": "d1a357a0228f351e0de068aa8a98213a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "576a8948664f31127d4a0b402bd9a5eb", "metric_id": "ef54acff80b88254826d0ec63ea35462", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}], "result_description": "In column NULL_PERCENT_STR, the last max_length value is 5. The average for this metric is 5."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_INT, the last null_percent value is 68. The average for this metric is 21.426.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Null Percent", "metrics": [{"value": 19.5, "average": 19.8335, "min_value": 18.418579331, "max_value": 21.248420669, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "aff3862a74da044d2e4d49f0bd67939b", "metric_id": "cda82b87563ef72a8a5958497501574b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_percent", "anomaly_score": -0.7071067812, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 19.5, "min_metric_value": 18.418579331, "max_metric_value": 21.248420669, "training_avg": 19.8335, "training_stddev": 0.4716402231, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_percent value is 19.5. The average for this metric is 19.834.", "is_anomalous": false}, {"value": 20.778, "average": 20.148333333, "min_value": 18.230719973, "max_value": 22.065946694, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "bbb596cdc938cc980890356df772085e", "metric_id": "d381ed3de7e1210e108b8b5a6992cccb", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_percent", "anomaly_score": 0.9850786603, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 20.778, "min_metric_value": 18.230719973, "max_metric_value": 22.065946694, "training_avg": 20.148333333, "training_stddev": 0.6392044535, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_percent value is 20.778. The average for this metric is 20.148.", "is_anomalous": false}, {"value": 20.556, "average": 20.25025, "min_value": 18.569349572, "max_value": 21.931150428, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "c98214d1240e5a0395fa0afb9c0d4388", "metric_id": "006efb3f8a8d4a97ae7b4451441ad725", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_percent", "anomaly_score": 0.54568967, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 20.556, "min_metric_value": 18.569349572, "max_metric_value": 21.931150428, "training_avg": 20.25025, "training_stddev": 0.5603001428, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_percent value is 20.556. The average for this metric is 20.25.", "is_anomalous": false}, {"value": 20.556, "average": 20.3114, "min_value": 18.799004814, "max_value": 21.823795186, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "d6df3ea69b9d94b7f91df7866553c468", "metric_id": "cdca04df0e19f3279f4ccc18b2812a83", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_percent", "anomaly_score": 0.4851906476, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 20.556, "min_metric_value": 18.799004814, "max_metric_value": 21.823795186, "training_avg": 20.3114, "training_stddev": 0.5041317288, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_percent value is 20.556. The average for this metric is 20.311.", "is_anomalous": false}, {"value": 21.0, "average": 20.426166667, "min_value": 18.832075609, "max_value": 22.020257724, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "cc34bcf3048a634948aacfa8e4f9f43f", "metric_id": "baa946cd0cb9a0078a736ebe381ef3f2", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_percent", "anomaly_score": 1.079925762, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 21.0, "min_metric_value": 18.832075609, "max_metric_value": 22.020257724, "training_avg": 20.426166667, "training_stddev": 0.5313636859, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_percent value is 21. The average for this metric is 20.426.", "is_anomalous": false}, {"value": 20.667, "average": 20.460571429, "min_value": 18.979971019, "max_value": 21.941171838, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "ab558a769766d2e32b99111ad9b5178f", "metric_id": "2adea55de30a4fc5945b1bfc5bc54531", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_percent", "anomaly_score": 0.4182666103, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 20.667, "min_metric_value": 18.979971019, "max_metric_value": 21.941171838, "training_avg": 20.460571429, "training_stddev": 0.4935334697, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_percent value is 20.667. The average for this metric is 20.461.", "is_anomalous": false}, {"value": 18.389, "average": 20.201625, "min_value": 17.611867569, "max_value": 22.791382431, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "a5dbbd894d715fa919837f16278cc77a", "metric_id": "1cc1405078277f858cdab476601d738e", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_percent", "anomaly_score": -2.099762292, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 18.389, "min_metric_value": 17.611867569, "max_metric_value": 22.791382431, "training_avg": 20.201625, "training_stddev": 0.8632524771, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_percent value is 18.389. The average for this metric is 20.202.", "is_anomalous": false}, {"value": 21.0, "average": 20.290333333, "min_value": 17.739668567, "max_value": 22.8409981, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "4ce2cb9327c619eca632d34c7e95d016", "metric_id": "ff75e7246361e2adac69feed52c5fa94", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_percent", "anomaly_score": 0.8346843647, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 21.0, "min_metric_value": 17.739668567, "max_metric_value": 22.8409981, "training_avg": 20.290333333, "training_stddev": 0.8502215888, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_percent value is 21. The average for this metric is 20.29.", "is_anomalous": false}, {"value": 20.333, "average": 20.2946, "min_value": 17.889469567, "max_value": 22.699730433, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "6b2e75edf6ca7eedac4557c742389135", "metric_id": "f2596f18aa2332b7472f8181826fecc6", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_percent", "anomaly_score": 0.04789761022, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 20.333, "min_metric_value": 17.889469567, "max_metric_value": 22.699730433, "training_avg": 20.2946, "training_stddev": 0.8017101444, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_percent value is 20.333. The average for this metric is 20.295.", "is_anomalous": false}, {"value": 20.5, "average": 20.313272727, "min_value": 18.024013991, "max_value": 22.602531464, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "7ed343c41a37e66ee2fe4f734676bea1", "metric_id": "ece0eceaf4aa71b4056365c027154e6a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_percent", "anomaly_score": 0.2447000897, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 20.5, "min_metric_value": 18.024013991, "max_metric_value": 22.602531464, "training_avg": 20.313272727, "training_stddev": 0.7630862456, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_percent value is 20.5. The average for this metric is 20.313.", "is_anomalous": false}, {"value": 19.278, "average": 20.227, "min_value": 17.86731358, "max_value": 22.58668642, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "ec87dfb78e8679ad64c0f44d6f2224bf", "metric_id": "d8969189e3743f579df23dd373f5b357", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_percent", "anomaly_score": -1.206516246, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 19.278, "min_metric_value": 17.86731358, "max_metric_value": 22.58668642, "training_avg": 20.227, "training_stddev": 0.7865621399, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_percent value is 19.278. The average for this metric is 20.227.", "is_anomalous": false}, {"value": 19.556, "average": 20.175384615, "min_value": 17.848194183, "max_value": 22.502575048, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "33f8f41d1549eec81951f400bd4d3ab6", "metric_id": "82a05802c39a8ccbf80c5502a441097f", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_percent", "anomaly_score": -0.798453715, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 19.556, "min_metric_value": 17.848194183, "max_metric_value": 22.502575048, "training_avg": 20.175384615, "training_stddev": 0.7757301441, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_percent value is 19.556. The average for this metric is 20.175.", "is_anomalous": false}, {"value": 19.389, "average": 20.119214286, "min_value": 17.796122062, "max_value": 22.442306509, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "008e7e14d8f9584ba397be0f98c7f069", "metric_id": "929720dd42d63ef80b3100815cb5b718", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_percent", "anomaly_score": -0.9429857477, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 19.389, "min_metric_value": 17.796122062, "max_metric_value": 22.442306509, "training_avg": 20.119214286, "training_stddev": 0.7743640744, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_percent value is 19.389. The average for this metric is 20.119.", "is_anomalous": false}, {"value": 19.111, "average": 20.052, "min_value": 17.681098815, "max_value": 22.422901185, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "79814856add0425bdbfddb296fbb1e69", "metric_id": "e4f0ec712114a96c5d6f9e1936d47adf", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_percent", "anomaly_score": -1.190686486, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 19.111, "min_metric_value": 17.681098815, "max_metric_value": 22.422901185, "training_avg": 20.052, "training_stddev": 0.790300395, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_percent value is 19.111. The average for this metric is 20.052.", "is_anomalous": false}, {"value": 19.278, "average": 20.003625, "min_value": 17.640701419, "max_value": 22.366548581, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "0eacd428fe9e9eef8292547562457145", "metric_id": "621fa7230e9132d291b15dce21ab0286", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_percent", "anomaly_score": -0.9212633948, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 19.278, "min_metric_value": 17.640701419, "max_metric_value": 22.366548581, "training_avg": 20.003625, "training_stddev": 0.7876411937, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_percent value is 19.278. The average for this metric is 20.004.", "is_anomalous": false}, {"value": 19.889, "average": 19.996882353, "min_value": 17.707471786, "max_value": 22.28629292, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "c7e9709639fb1ebd894c23418a52afe8", "metric_id": "0e3c3fc0506faba42775f41a02d00e8c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_percent", "anomaly_score": -0.1413669805, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 19.889, "min_metric_value": 17.707471786, "max_metric_value": 22.28629292, "training_avg": 19.996882353, "training_stddev": 0.7631368555, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_percent value is 19.889. The average for this metric is 19.997.", "is_anomalous": false}, {"value": 19.167, "average": 19.950777778, "min_value": 17.653510834, "max_value": 22.248044722, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "d9ad6c761899d6412f6680c13a57aefe", "metric_id": "038dc92274aafd0fbe155a0525344fab", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_percent", "anomaly_score": -1.023535092, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 19.167, "min_metric_value": 17.653510834, "max_metric_value": 22.248044722, "training_avg": 19.950777778, "training_stddev": 0.765755648, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_percent value is 19.167. The average for this metric is 19.951.", "is_anomalous": false}, {"value": 20.278, "average": 19.968, "min_value": 17.724127455, "max_value": 22.211872545, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "ee792d00403ef954fda5071ae06cf4f3", "metric_id": "61ea5fc45ad089ea1287cdcf8d9a2cb3", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_percent", "anomaly_score": 0.4144620433, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 20.278, "min_metric_value": 17.724127455, "max_metric_value": 22.211872545, "training_avg": 19.968, "training_stddev": 0.7479575151, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_percent value is 20.278. The average for this metric is 19.968.", "is_anomalous": false}, {"value": 18.278, "average": 19.8835, "min_value": 17.422766188, "max_value": 22.344233812, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "0e5d5595a0ad0a84750503da508001bf", "metric_id": "bdb56a01dda5b1ace89c8bb7ed5ae0e3", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_percent", "anomaly_score": -1.957342958, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 18.278, "min_metric_value": 17.422766188, "max_metric_value": 22.344233812, "training_avg": 19.8835, "training_stddev": 0.8202446041, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_percent value is 18.278. The average for this metric is 19.884.", "is_anomalous": false}, {"value": 20.778, "average": 19.926095238, "min_value": 17.457216279, "max_value": 22.394974197, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "dc6a1de53da5042ba95d51e287840286", "metric_id": "092bc97797edfe8e1c48e684d8bb13d5", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_percent", "anomaly_score": 1.035171966, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 20.778, "min_metric_value": 17.457216279, "max_metric_value": 22.394974197, "training_avg": 19.926095238, "training_stddev": 0.822959653, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_percent value is 20.778. The average for this metric is 19.926.", "is_anomalous": false}, {"value": 19.278, "average": 19.896636364, "min_value": 17.451858802, "max_value": 22.341413925, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "2418e573f7eaf81c367601e0708ef1a4", "metric_id": "189b12e033f364dbb21ca807f2debdab", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_percent", "anomaly_score": -0.7591320864, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 19.278, "min_metric_value": 17.451858802, "max_metric_value": 22.341413925, "training_avg": 19.896636364, "training_stddev": 0.8149258538, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_percent value is 19.278. The average for this metric is 19.897.", "is_anomalous": false}, {"value": 19.111, "average": 19.862478261, "min_value": 17.423876001, "max_value": 22.301080521, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "139db445c883705bff929684ce0de8be", "metric_id": "d86f1aa7e1c023476f5e1a2ff9a80122", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_percent", "anomaly_score": -0.9244782633, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 19.111, "min_metric_value": 17.423876001, "max_metric_value": 22.301080521, "training_avg": 19.862478261, "training_stddev": 0.8128674201, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_percent value is 19.111. The average for this metric is 19.862.", "is_anomalous": false}, {"value": 19.111, "average": 19.831166667, "min_value": 17.402176109, "max_value": 22.260157225, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "083905d19deec400df231f8a4457412f", "metric_id": "c103b9a1b6cf6dffb90183dc32cb0712", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_percent", "anomaly_score": -0.889464141, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 19.111, "min_metric_value": 17.402176109, "max_metric_value": 22.260157225, "training_avg": 19.831166667, "training_stddev": 0.8096635193, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_percent value is 19.111. The average for this metric is 19.831.", "is_anomalous": false}, {"value": 20.222, "average": 19.8468, "min_value": 17.457416764, "max_value": 22.236183236, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "f46f3d22a523845f54b2e84739595197", "metric_id": "d17e8f88236ccde7e0518696832c973c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_percent", "anomaly_score": 0.4710839111, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 20.222, "min_metric_value": 17.457416764, "max_metric_value": 22.236183236, "training_avg": 19.8468, "training_stddev": 0.7964610788, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_percent value is 20.222. The average for this metric is 19.847.", "is_anomalous": false}, {"value": 19.444, "average": 19.831307692, "min_value": 17.478235472, "max_value": 22.184379913, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "a7a9d57bf380618d73847bf09a6e4662", "metric_id": "96c82f35bc9897f527c54292eeacfc12", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_percent", "anomaly_score": -0.4937898068, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 19.444, "min_metric_value": 17.478235472, "max_metric_value": 22.184379913, "training_avg": 19.831307692, "training_stddev": 0.7843574068, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_percent value is 19.444. The average for this metric is 19.831.", "is_anomalous": false}, {"value": 20.778, "average": 19.86637037, "min_value": 17.495140434, "max_value": 22.237600307, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "59693510109df4b33e0fe296f00992e4", "metric_id": "d58685b2d2847f0f4311aab8dda9c981", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_percent", "anomaly_score": 1.153363007, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 20.778, "min_metric_value": 17.495140434, "max_metric_value": 22.237600307, "training_avg": 19.86637037, "training_stddev": 0.7904099787, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_percent value is 20.778. The average for this metric is 19.866.", "is_anomalous": false}, {"value": 20.056, "average": 19.873142857, "min_value": 17.54375657, "max_value": 22.202529145, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "d1a59f4c26e905dfbcc89cee2a85f592", "metric_id": "04a04ca45471e46e47046372758ef48c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_percent", "anomaly_score": 0.2355004112, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 20.056, "min_metric_value": 17.54375657, "max_metric_value": 22.202529145, "training_avg": 19.873142857, "training_stddev": 0.7764620958, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_percent value is 20.056. The average for this metric is 19.873.", "is_anomalous": false}, {"value": 18.333, "average": 19.820034483, "min_value": 17.377002828, "max_value": 22.263066138, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "d06044a5f3f1fa2b60e461f269444e35", "metric_id": "1c848c7d89eadb3538ee9ea00fb9fe2a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_percent", "anomaly_score": -1.826052249, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 18.333, "min_metric_value": 17.377002828, "max_metric_value": 22.263066138, "training_avg": 19.820034483, "training_stddev": 0.814343885, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_percent value is 18.333. The average for this metric is 19.82.", "is_anomalous": false}, {"value": 68.0, "average": 21.426033333, "min_value": 17.377002828, "max_value": 22.263066138, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "ab740302354c0f2fe191ad5baa940b8c", "metric_id": "da4fce36f2ff9d68c4490e1e03873c63", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "null_percent", "anomaly_score": 5.272879996, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 68.0, "min_metric_value": -5.072180188, "max_metric_value": 47.924246855, "training_avg": 21.426033333, "training_stddev": 8.83273784, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last null_percent value is 68. The average for this metric is 21.426.", "is_anomalous": true}], "result_description": "In column NULL_PERCENT_INT, the last null_percent value is 68. The average for this metric is 21.426."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "standard_deviation", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('standard_deviation' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_FLOAT, the last standard_deviation value is 2.282. The average for this metric is 2.226.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('standard_deviation' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Standard Deviation", "metrics": [{"value": 2.277735527, "average": 2.278725987, "min_value": 2.274523822, "max_value": 2.282928152, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "2f3af9da0d366e61ca6303d08d85624f", "metric_id": "d45e78ebf10bd3368f103776846bb053", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": -0.7071067812, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 2.277735527, "min_metric_value": 2.274523822, "max_metric_value": 2.282928152, "training_avg": 2.278725987, "training_stddev": 0.001400721794, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last standard_deviation value is 2.278. The average for this metric is 2.279.", "is_anomalous": false}, {"value": 2.205655442, "average": 2.254369139, "min_value": 2.127772368, "max_value": 2.38096591, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "0d38ef84b74e2c751176de4281b86194", "metric_id": "ffedb5cc051de38a2e1f2bd639c629cc", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": -1.154382434, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 2.205655442, "min_metric_value": 2.127772368, "max_metric_value": 2.38096591, "training_avg": 2.254369139, "training_stddev": 0.04219892375, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last standard_deviation value is 2.206. The average for this metric is 2.254.", "is_anomalous": false}, {"value": 2.255437925, "average": 2.254636335, "min_value": 2.151258073, "max_value": 2.358014598, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "6699d06efbdca880e3765bc4397b2b15", "metric_id": "45b4d58392f1f4fd45398f623261279c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": 0.02326183984, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 2.255437925, "min_metric_value": 2.151258073, "max_metric_value": 2.358014598, "training_avg": 2.254636335, "training_stddev": 0.03445942087, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last standard_deviation value is 2.255. The average for this metric is 2.255.", "is_anomalous": false}, {"value": 2.275912433, "average": 2.258891555, "min_value": 2.164922895, "max_value": 2.352860215, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "be73bffbe0fd60f31c15955542d347ba", "metric_id": "fbe084b090a16ca53f620bcaddc1c18d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": 0.543400677, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 2.275912433, "min_metric_value": 2.164922895, "max_metric_value": 2.352860215, "training_avg": 2.258891555, "training_stddev": 0.03132288665, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last standard_deviation value is 2.276. The average for this metric is 2.259.", "is_anomalous": false}, {"value": 2.245558685, "average": 2.25666941, "min_value": 2.171049696, "max_value": 2.342289123, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "b1f851ac63fdc0ca7d4350bbcf0c2acb", "metric_id": "34e4c499fce058225256bfb77b611595", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": -0.3893048948, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 2.245558685, "min_metric_value": 2.171049696, "max_metric_value": 2.342289123, "training_avg": 2.25666941, "training_stddev": 0.02853990452, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last standard_deviation value is 2.246. The average for this metric is 2.257.", "is_anomalous": false}, {"value": 2.211100142, "average": 2.250159514, "min_value": 2.156464228, "max_value": 2.343854801, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "f8d6d536d3d69fec2f9df99cdbd5f0d1", "metric_id": "99c3e34a77572d110ceaeb40d8010b50", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": -1.250629806, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 2.211100142, "min_metric_value": 2.156464228, "max_metric_value": 2.343854801, "training_avg": 2.250159514, "training_stddev": 0.03123176204, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last standard_deviation value is 2.211. The average for this metric is 2.25.", "is_anomalous": false}, {"value": 2.231344457, "average": 2.247807632, "min_value": 2.158796686, "max_value": 2.336818579, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "f6cacba666ac9d48d58b932814ebfd40", "metric_id": "8dff7cbbf12e2f42c1a2ab2ff54404b1", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": -0.5548702497, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 2.231344457, "min_metric_value": 2.158796686, "max_metric_value": 2.336818579, "training_avg": 2.247807632, "training_stddev": 0.0296703155, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last standard_deviation value is 2.231. The average for this metric is 2.248.", "is_anomalous": false}, {"value": 2.187709099, "average": 2.241130017, "min_value": 2.138444022, "max_value": 2.343816013, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "e2da9323e01efb747c9314ca467429c3", "metric_id": "efd1da13c66031e16ab41d10dcfcbe5b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": -1.560707025, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 2.187709099, "min_metric_value": 2.138444022, "max_metric_value": 2.343816013, "training_avg": 2.241130017, "training_stddev": 0.0342286652, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last standard_deviation value is 2.188. The average for this metric is 2.241.", "is_anomalous": false}, {"value": 2.220877261, "average": 2.239104742, "min_value": 2.140403329, "max_value": 2.337806155, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "9e512bca4186f9f6f0157fd13f639452", "metric_id": "776de297554ead09e105e78a1be415e3", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": -0.5540188499, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 2.220877261, "min_metric_value": 2.140403329, "max_metric_value": 2.337806155, "training_avg": 2.239104742, "training_stddev": 0.03290047097, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last standard_deviation value is 2.221. The average for this metric is 2.239.", "is_anomalous": false}, {"value": 2.259284538, "average": 2.240939269, "min_value": 2.145540341, "max_value": 2.336338196, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "056f4f5d84f508fdd0cf5c8b62cf92be", "metric_id": "ab565fc7e1a29b792e1ec9ebb133a854", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": 0.5769017315, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 2.259284538, "min_metric_value": 2.145540341, "max_metric_value": 2.336338196, "training_avg": 2.240939269, "training_stddev": 0.03179964259, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last standard_deviation value is 2.259. The average for this metric is 2.241.", "is_anomalous": false}, {"value": 2.193301449, "average": 2.23696945, "min_value": 2.137091439, "max_value": 2.336847462, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "0e4f52dcb98101ccf57fc3583d9793b2", "metric_id": "b5a7890bf01a50610fe758bc691512b0", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": -1.311640082, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 2.193301449, "min_metric_value": 2.137091439, "max_metric_value": 2.336847462, "training_avg": 2.23696945, "training_stddev": 0.03329267056, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last standard_deviation value is 2.193. The average for this metric is 2.237.", "is_anomalous": false}, {"value": 2.184421953, "average": 2.232927335, "min_value": 2.12778009, "max_value": 2.33807458, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "7fc62ef668fe643415dcb50a37f02177", "metric_id": "9674871603ac83b5a33a5d418eb4e887", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": -1.383927338, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 2.184421953, "min_metric_value": 2.12778009, "max_metric_value": 2.33807458, "training_avg": 2.232927335, "training_stddev": 0.03504908165, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last standard_deviation value is 2.184. The average for this metric is 2.233.", "is_anomalous": false}, {"value": 2.26301554, "average": 2.235076493, "min_value": 2.131213773, "max_value": 2.338939213, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "541d3da2010399d5cf94eddb373e2f83", "metric_id": "14e11a2e33d652ac9759b318877484ba", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": 0.8069993037, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 2.26301554, "min_metric_value": 2.131213773, "max_metric_value": 2.338939213, "training_avg": 2.235076493, "training_stddev": 0.03462090663, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last standard_deviation value is 2.263. The average for this metric is 2.235.", "is_anomalous": false}, {"value": 2.255257704, "average": 2.236421907, "min_value": 2.135123831, "max_value": 2.337719983, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "7eb47d46ac2cd22c23445813305bec22", "metric_id": "a60d8d7c2477c2aae4494b059700eec3", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": 0.5578328234, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 2.255257704, "min_metric_value": 2.135123831, "max_metric_value": 2.337719983, "training_avg": 2.236421907, "training_stddev": 0.03376602529, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last standard_deviation value is 2.255. The average for this metric is 2.236.", "is_anomalous": false}, {"value": 2.249579819, "average": 2.237244276, "min_value": 2.138884734, "max_value": 2.335603818, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "6eebd016db9447016964ab5ccb621d50", "metric_id": "9bb61da749408c057b2b629908d48dfa", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": 0.3762383076, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 2.249579819, "min_metric_value": 2.138884734, "max_metric_value": 2.335603818, "training_avg": 2.237244276, "training_stddev": 0.03278651397, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last standard_deviation value is 2.25. The average for this metric is 2.237.", "is_anomalous": false}, {"value": 2.179047407, "average": 2.233820931, "min_value": 2.129595268, "max_value": 2.338046594, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "c233092d367609086a39da20333b38de", "metric_id": "627f53c2223294d26a04d10e5386f352", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": -1.576584575, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 2.179047407, "min_metric_value": 2.129595268, "max_metric_value": 2.338046594, "training_avg": 2.233820931, "training_stddev": 0.03474188764, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last standard_deviation value is 2.179. The average for this metric is 2.234.", "is_anomalous": false}, {"value": 2.258393654, "average": 2.235186082, "min_value": 2.132590279, "max_value": 2.337781886, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "89917d60ae1c96b7237ab81f56aac862", "metric_id": "ced37aaaec42e71277d62dcde19219e4", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": 0.6786117378, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 2.258393654, "min_metric_value": 2.132590279, "max_metric_value": 2.337781886, "training_avg": 2.235186082, "training_stddev": 0.03419860107, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last standard_deviation value is 2.258. The average for this metric is 2.235.", "is_anomalous": false}, {"value": 2.189876276, "average": 2.232801356, "min_value": 2.128333226, "max_value": 2.337269486, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "df0b18a24b6bc436ae98860023e35168", "metric_id": "3cd50357939a2c3e660166246da875ed", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": -1.232674876, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 2.189876276, "min_metric_value": 2.128333226, "max_metric_value": 2.337269486, "training_avg": 2.232801356, "training_stddev": 0.03482271003, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last standard_deviation value is 2.19. The average for this metric is 2.233.", "is_anomalous": false}, {"value": 2.215801578, "average": 2.231951367, "min_value": 2.129632076, "max_value": 2.334270658, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "f87e715e3eab548efd15a9bd92a2d223", "metric_id": "1041b2c41007669e10c72f1c887f22de", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": -0.4735115508, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 2.215801578, "min_metric_value": 2.129632076, "max_metric_value": 2.334270658, "training_avg": 2.231951367, "training_stddev": 0.03410643035, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last standard_deviation value is 2.216. The average for this metric is 2.232.", "is_anomalous": false}, {"value": 2.209196042, "average": 2.23086778, "min_value": 2.130032807, "max_value": 2.331702753, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "3858f53bad0a7a5a23789c21cf84b5bd", "metric_id": "bc5ed59976cb87c9216dfcaae44e15e1", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": -0.6447684945, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 2.209196042, "min_metric_value": 2.130032807, "max_metric_value": 2.331702753, "training_avg": 2.23086778, "training_stddev": 0.0336116577, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last standard_deviation value is 2.209. The average for this metric is 2.231.", "is_anomalous": false}, {"value": 2.172828732, "average": 2.228229641, "min_value": 2.123055731, "max_value": 2.333403552, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "e2fdbec9461a371faee996d1050bfe18", "metric_id": "61d7b2cd96de75097b019dfbe65f9898", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": -1.580265745, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 2.172828732, "min_metric_value": 2.123055731, "max_metric_value": 2.333403552, "training_avg": 2.228229641, "training_stddev": 0.03505797015, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last standard_deviation value is 2.173. The average for this metric is 2.228.", "is_anomalous": false}, {"value": 2.17202853, "average": 2.225786115, "min_value": 2.117182644, "max_value": 2.334389585, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "038ff930da8d6d07b896b876d5e52ef0", "metric_id": "f41f96cdc976f517f6a5a634caf278e0", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": -1.484968696, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 2.17202853, "min_metric_value": 2.117182644, "max_metric_value": 2.334389585, "training_avg": 2.225786115, "training_stddev": 0.03620115687, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last standard_deviation value is 2.172. The average for this metric is 2.226.", "is_anomalous": false}, {"value": 2.219509136, "average": 2.225524574, "min_value": 2.119238755, "max_value": 2.331810393, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "54b2b4c5e0b1be87636b63de152ce295", "metric_id": "8e11d28ead69045c360bc602d4f4f049", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": -0.1697904276, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 2.219509136, "min_metric_value": 2.119238755, "max_metric_value": 2.331810393, "training_avg": 2.225524574, "training_stddev": 0.03542860639, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last standard_deviation value is 2.22. The average for this metric is 2.226.", "is_anomalous": false}, {"value": 2.243871086, "average": 2.226258434, "min_value": 2.121629784, "max_value": 2.330887085, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "0aae974d92f0762357db11ff42f6f0ae", "metric_id": "8de61573368d7a82a053b1eb99798fd6", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": 0.5050046468, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 2.243871086, "min_metric_value": 2.121629784, "max_metric_value": 2.330887085, "training_avg": 2.226258434, "training_stddev": 0.03487621694, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last standard_deviation value is 2.244. The average for this metric is 2.226.", "is_anomalous": false}, {"value": 2.188021591, "average": 2.224787787, "min_value": 2.119833679, "max_value": 2.329741895, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "e63f4c75b375b796083fb2693e25c013", "metric_id": "04bd4479c4916a0b915dc494e267143c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": -1.050922058, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 2.188021591, "min_metric_value": 2.119833679, "max_metric_value": 2.329741895, "training_avg": 2.224787787, "training_stddev": 0.03498470264, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last standard_deviation value is 2.188. The average for this metric is 2.225.", "is_anomalous": false}, {"value": 2.273600375, "average": 2.22659566, "min_value": 2.119890835, "max_value": 2.333300486, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "5ad4caf0f02107b40c33f4ae79161d9b", "metric_id": "ee7b120dccc7d442b2b196e0b320e2c2", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": 1.32153484, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 2.273600375, "min_metric_value": 2.119890835, "max_metric_value": 2.333300486, "training_avg": 2.22659566, "training_stddev": 0.03556827523, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last standard_deviation value is 2.274. The average for this metric is 2.227.", "is_anomalous": false}, {"value": 2.222745824, "average": 2.226458166, "min_value": 2.121725253, "max_value": 2.331191079, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "1e5b47c2ca2af2a82a75311e5eec3f50", "metric_id": "95520980952e5007b645d58e54580cb2", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": -0.1063374048, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 2.222745824, "min_metric_value": 2.121725253, "max_metric_value": 2.331191079, "training_avg": 2.226458166, "training_stddev": 0.03491097099, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last standard_deviation value is 2.223. The average for this metric is 2.226.", "is_anomalous": false}, {"value": 2.155589557, "average": 2.224014421, "min_value": 2.113851374, "max_value": 2.334177468, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "7a4099069c11c345306dc15c068aa81a", "metric_id": "e83c3893b5ca5c5d08824978ca26605d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": -1.863370665, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 2.155589557, "min_metric_value": 2.113851374, "max_metric_value": 2.334177468, "training_avg": 2.224014421, "training_stddev": 0.03672101582, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last standard_deviation value is 2.156. The average for this metric is 2.224.", "is_anomalous": false}, {"value": 2.281733959, "average": 2.225938406, "min_value": 2.113169255, "max_value": 2.338707556, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "19cec67f9a5c45383371a36517d8a702", "metric_id": "99f9f3e37c1bdf7ace37e2783a1c9654", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "standard_deviation", "anomaly_score": 1.484330227, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 2.281733959, "min_metric_value": 2.113169255, "max_metric_value": 2.338707556, "training_avg": 2.225938406, "training_stddev": 0.03758971686, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last standard_deviation value is 2.282. The average for this metric is 2.226.", "is_anomalous": false}], "result_description": "In column NULL_PERCENT_FLOAT, the last standard_deviation value is 2.282. The average for this metric is 2.226."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "average", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_FLOAT, the last average value is 5.094. The average for this metric is 5.062.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Average", "metrics": [{"value": 5.044987025, "average": 5.055165454, "min_value": 5.011982037, "max_value": 5.098348871, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "184a47e54e94afd3a051ffc9a3665247", "metric_id": "fae10e1c0547d73e973ed4b5524d504b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "average", "anomaly_score": -0.7071067812, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 5.044987025, "min_metric_value": 5.011982037, "max_metric_value": 5.098348871, "training_avg": 5.055165454, "training_stddev": 0.01439447244, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last average value is 5.045. The average for this metric is 5.055.", "is_anomalous": false}, {"value": 5.109052952, "average": 5.073127953, "min_value": 4.974924129, "max_value": 5.171331778, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "2cbcb296e9f9feca359c103dc0afaf25", "metric_id": "c85d4215e93066b25e4bd06f774d25c0", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "average", "anomaly_score": 1.097462313, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 5.109052952, "min_metric_value": 4.974924129, "max_metric_value": 5.171331778, "training_avg": 5.073127953, "training_stddev": 0.03273460825, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last average value is 5.109. The average for this metric is 5.073.", "is_anomalous": false}, {"value": 5.053206863, "average": 5.068147681, "min_value": 4.982577595, "max_value": 5.153717767, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "15e8e0b07ffe37302bd378c313083af4", "metric_id": "913505ed569b8c6554b1652da9406680", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "average", "anomaly_score": -0.5238098347, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 5.053206863, "min_metric_value": 4.982577595, "max_metric_value": 5.153717767, "training_avg": 5.068147681, "training_stddev": 0.02852336191, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last average value is 5.053. The average for this metric is 5.068.", "is_anomalous": false}, {"value": 5.082412915, "average": 5.071000728, "min_value": 4.994463324, "max_value": 5.147538131, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "fb6e4aa2c8c15906dd3e4a08a8611777", "metric_id": "69f9c24a0220b989ed8a092fbc47481a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "average", "anomaly_score": 0.4473180476, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 5.082412915, "min_metric_value": 4.994463324, "max_metric_value": 5.147538131, "training_avg": 5.071000728, "training_stddev": 0.02551246777, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last average value is 5.082. The average for this metric is 5.071.", "is_anomalous": false}, {"value": 5.064137456, "average": 5.069856849, "min_value": 5.00088558, "max_value": 5.138828119, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "ca36b92013a5821efb07913ed848d3b7", "metric_id": "53718d4aebab37176f06109431be6b41", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "average", "anomaly_score": -0.2487728339, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 5.064137456, "min_metric_value": 5.00088558, "max_metric_value": 5.138828119, "training_avg": 5.069856849, "training_stddev": 0.02299042318, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last average value is 5.064. The average for this metric is 5.07.", "is_anomalous": false}, {"value": 5.10549233, "average": 5.074947632, "min_value": 5.000135139, "max_value": 5.149760125, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "074c17164e91d3e211276f7a1016681f", "metric_id": "efd7ef185a86192bb1fd5067c72c4709", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "average", "anomaly_score": 1.224850135, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 5.10549233, "min_metric_value": 5.000135139, "max_metric_value": 5.149760125, "training_avg": 5.074947632, "training_stddev": 0.02493749772, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last average value is 5.105. The average for this metric is 5.075.", "is_anomalous": false}, {"value": 5.106117386, "average": 5.078843851, "min_value": 5.002095246, "max_value": 5.155592457, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "4f453717701942c4e09e2286dfccbe07", "metric_id": "4a46664723f7485fb0da0725781b8a7d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "average", "anomaly_score": 1.066085864, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 5.106117386, "min_metric_value": 5.002095246, "max_metric_value": 5.155592457, "training_avg": 5.078843851, "training_stddev": 0.02558286844, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last average value is 5.106. The average for this metric is 5.079.", "is_anomalous": false}, {"value": 4.997918622, "average": 5.069852159, "min_value": 4.961671997, "max_value": 5.178032321, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "e7eb3bafaf6888fcb511dc4753976bf2", "metric_id": "41db293f6b4654e9a30375ee16abea88", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "average", "anomaly_score": -1.994826089, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 4.997918622, "min_metric_value": 4.961671997, "max_metric_value": 5.178032321, "training_avg": 5.069852159, "training_stddev": 0.03606005411, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last average value is 4.998. The average for this metric is 5.07.", "is_anomalous": false}, {"value": 4.983058765, "average": 5.06117282, "min_value": 4.93009115, "max_value": 5.192254489, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "b2a0739bcd0c489eba74182c5ad719e1", "metric_id": "2dedfc3ba6bb1c62f21a5826beaf6a54", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "average", "anomaly_score": -1.787756951, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 4.983058765, "min_metric_value": 4.93009115, "max_metric_value": 5.192254489, "training_avg": 5.06117282, "training_stddev": 0.04369388978, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last average value is 4.983. The average for this metric is 5.061.", "is_anomalous": false}, {"value": 5.19193481, "average": 5.073060273, "min_value": 4.901438574, "max_value": 5.244681972, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "8d842306f70ff2d4d42ce9730538e9af", "metric_id": "6f2acb874dbf8c1bdd12a3125fa44c3c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "average", "anomaly_score": 2.077963408, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 5.19193481, "min_metric_value": 4.901438574, "max_metric_value": 5.244681972, "training_avg": 5.073060273, "training_stddev": 0.05720723303, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last average value is 5.192. The average for this metric is 5.073.", "is_anomalous": false}, {"value": 5.082021308, "average": 5.073807026, "min_value": 4.909988237, "max_value": 5.237625815, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "99c79b63a8b8c65569add11169740819", "metric_id": "7df224566ea1ab5024a349a0993b6ee3", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "average", "anomaly_score": 0.1504274637, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 5.082021308, "min_metric_value": 4.909988237, "max_metric_value": 5.237625815, "training_avg": 5.073807026, "training_stddev": 0.05460626302, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last average value is 5.082. The average for this metric is 5.074.", "is_anomalous": false}, {"value": 5.050247135, "average": 5.071994727, "min_value": 4.913929896, "max_value": 5.230059558, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "762a4cd27b65330ccb266f960e96b944", "metric_id": "c129007fc76b25c7663fe599f7398df6", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "average", "anomaly_score": -0.4127595922, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 5.050247135, "min_metric_value": 4.913929896, "max_metric_value": 5.230059558, "training_avg": 5.071994727, "training_stddev": 0.05268827705, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last average value is 5.05. The average for this metric is 5.072.", "is_anomalous": false}, {"value": 5.03060613, "average": 5.069038399, "min_value": 4.9135912, "max_value": 5.224485597, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "777bb3b27bf75828a3fad49c4e7aeca8", "metric_id": "7c2c0f419fe747ac89b7a8e2932276ef", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "average", "anomaly_score": -0.7417104146, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 5.03060613, "min_metric_value": 4.9135912, "max_metric_value": 5.224485597, "training_avg": 5.069038399, "training_stddev": 0.0518157328, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last average value is 5.031. The average for this metric is 5.069.", "is_anomalous": false}, {"value": 5.073567738, "average": 5.069340355, "min_value": 4.919506605, "max_value": 5.219174104, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "80e252e06483b6d99faad051451276cb", "metric_id": "fc778504ac2cd2d6e4424a3dee3b1ce3", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "average", "anomaly_score": 0.08464147407, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 5.073567738, "min_metric_value": 4.919506605, "max_metric_value": 5.219174104, "training_avg": 5.069340355, "training_stddev": 0.04994458315, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last average value is 5.074. The average for this metric is 5.069.", "is_anomalous": false}, {"value": 5.070869095, "average": 5.069435901, "min_value": 4.924678206, "max_value": 5.214193596, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "64216404d8381f85d8f19b9d5317f8ca", "metric_id": "e0a3e436032be905f58bddc1db7fe76a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "average", "anomaly_score": 0.02970192475, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 5.070869095, "min_metric_value": 4.924678206, "max_metric_value": 5.214193596, "training_avg": 5.069435901, "training_stddev": 0.04825256496, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last average value is 5.071. The average for this metric is 5.069.", "is_anomalous": false}, {"value": 5.063428001, "average": 5.069082495, "min_value": 4.928853308, "max_value": 5.209311682, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "9d1abe471be912d9fdc9cf192d1b510a", "metric_id": "7a4ac61306983b17fb53e1ecee6be02d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "average", "anomaly_score": -0.1209697029, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 5.063428001, "min_metric_value": 4.928853308, "max_metric_value": 5.209311682, "training_avg": 5.069082495, "training_stddev": 0.04674306233, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last average value is 5.063. The average for this metric is 5.069.", "is_anomalous": false}, {"value": 5.110656265, "average": 5.071392149, "min_value": 4.932209912, "max_value": 5.210574385, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "2a013ae2518a4730f5904902ac34f43c", "metric_id": "655b8b46a91d5c8de3ffaf34da37a309", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "average", "anomaly_score": 0.8463174051, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 5.110656265, "min_metric_value": 4.932209912, "max_metric_value": 5.210574385, "training_avg": 5.071392149, "training_stddev": 0.04639407884, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last average value is 5.111. The average for this metric is 5.071.", "is_anomalous": false}, {"value": 5.075268795, "average": 5.071596183, "min_value": 4.93630905, "max_value": 5.206883316, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "bcfe532e27cddbaf24d35883b3464c0d", "metric_id": "9d72e469c521aa73a1d37167507f7722", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "average", "anomaly_score": 0.08144038829, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 5.075268795, "min_metric_value": 4.93630905, "max_metric_value": 5.206883316, "training_avg": 5.071596183, "training_stddev": 0.04509571105, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last average value is 5.075. The average for this metric is 5.072.", "is_anomalous": false}, {"value": 5.048003644, "average": 5.070416556, "min_value": 4.937790062, "max_value": 5.203043049, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "aa790b3aa8b5eb1db064bd9b01aa9842", "metric_id": "874dacd11cad4c029af56e5e556e8050", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "average", "anomaly_score": -0.5069781567, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 5.048003644, "min_metric_value": 4.937790062, "max_metric_value": 5.203043049, "training_avg": 5.070416556, "training_stddev": 0.04420883115, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last average value is 5.048. The average for this metric is 5.07.", "is_anomalous": false}, {"value": 5.021007085, "average": 5.068063724, "min_value": 4.934809955, "max_value": 5.201317493, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "809039cbb021e2bc9bccb00bec39e931", "metric_id": "5a1d8e8b5432b4793c82fb4b32422d85", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "average", "anomaly_score": -1.059406547, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 5.021007085, "min_metric_value": 4.934809955, "max_metric_value": 5.201317493, "training_avg": 5.068063724, "training_stddev": 0.04441792311, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last average value is 5.021. The average for this metric is 5.068.", "is_anomalous": false}, {"value": 5.025876408, "average": 5.066146119, "min_value": 4.933333831, "max_value": 5.198958407, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "06a3d2c113fbcf565d13d595a2fe739b", "metric_id": "1eaa683695dda3bb9c121b8b11b30535", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "average", "anomaly_score": -0.9096231535, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 5.025876408, "min_metric_value": 4.933333831, "max_metric_value": 5.198958407, "training_avg": 5.066146119, "training_stddev": 0.04427076262, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last average value is 5.026. The average for this metric is 5.066.", "is_anomalous": false}, {"value": 5.075852806, "average": 5.066568149, "min_value": 4.936667437, "max_value": 5.196468861, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "05247b3abefb35dc717da34f1e1da0d5", "metric_id": "4194899b9374399003157ab8d18d6283", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "average", "anomaly_score": 0.2144250821, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 5.075852806, "min_metric_value": 4.936667437, "max_metric_value": 5.196468861, "training_avg": 5.066568149, "training_stddev": 0.04330023732, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last average value is 5.076. The average for this metric is 5.067.", "is_anomalous": false}, {"value": 5.030902336, "average": 5.065082073, "min_value": 4.936172984, "max_value": 5.193991162, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "a34a477823bf83bac37a2d5b822d0adc", "metric_id": "886cfcb850a0044d5c8a3010b0997e5c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "average", "anomaly_score": -0.7954381723, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 5.030902336, "min_metric_value": 4.936172984, "max_metric_value": 5.193991162, "training_avg": 5.065082073, "training_stddev": 0.0429696962, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last average value is 5.031. The average for this metric is 5.065.", "is_anomalous": false}, {"value": 4.926740401, "average": 5.059548406, "min_value": 4.908502103, "max_value": 5.210594709, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "865fd6fa3ab418fe66301a40b308a2af", "metric_id": "6a8690787a5abac65ae6553cfde8383a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "average", "anomaly_score": -2.637760797, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 4.926740401, "min_metric_value": 4.908502103, "max_metric_value": 5.210594709, "training_avg": 5.059548406, "training_stddev": 0.05034876771, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last average value is 4.927. The average for this metric is 5.06.", "is_anomalous": false}, {"value": 5.103926817, "average": 5.061255268, "min_value": 4.910975141, "max_value": 5.211535395, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "30c85673fdc43bf32c7859ea0edc47f9", "metric_id": "48579829a0f5f8fffae3e74d7a32d88b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "average", "anomaly_score": 0.8518401518, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 5.103926817, "min_metric_value": 4.910975141, "max_metric_value": 5.211535395, "training_avg": 5.061255268, "training_stddev": 0.05009337575, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last average value is 5.104. The average for this metric is 5.061.", "is_anomalous": false}, {"value": 5.066681994, "average": 5.061456258, "min_value": 4.914061166, "max_value": 5.20885135, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "d1d30afea7ba466376c3add0675e2479", "metric_id": "6569ea4e12111dc3d37cd4b37db67d1e", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "average", "anomaly_score": 0.1063618042, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 5.066681994, "min_metric_value": 4.914061166, "max_metric_value": 5.20885135, "training_avg": 5.061456258, "training_stddev": 0.04913169742, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last average value is 5.067. The average for this metric is 5.061.", "is_anomalous": false}, {"value": 4.966052741, "average": 5.058048989, "min_value": 4.903626645, "max_value": 5.212471334, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "6db36599160fdf70f5bbef48343c6cb0", "metric_id": "34d052e922475b17149277bfcbb9464f", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "average", "anomaly_score": -1.787233228, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 4.966052741, "min_metric_value": 4.903626645, "max_metric_value": 5.212471334, "training_avg": 5.058048989, "training_stddev": 0.05147411491, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last average value is 4.966. The average for this metric is 5.058.", "is_anomalous": false}, {"value": 5.148359116, "average": 5.061163132, "min_value": 4.901395314, "max_value": 5.22093095, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "139c9a40f63afb2189caa74e26d4025b", "metric_id": "46e711ed3f0d2b9c8ba45fc6e92e79f1", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "average", "anomaly_score": 1.63730065, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 5.148359116, "min_metric_value": 4.901395314, "max_metric_value": 5.22093095, "training_avg": 5.061163132, "training_stddev": 0.0532559393, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last average value is 5.148. The average for this metric is 5.061.", "is_anomalous": false}, {"value": 5.093834999, "average": 5.062252194, "min_value": 4.904246522, "max_value": 5.220257866, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "7253c9f41bfb2b5d84a41fc6b023fb4c", "metric_id": "af2f88e696f811e1f8fc57509b95d80f", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_FLOAT", "metric_name": "average", "anomaly_score": 0.5996519769, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 5.093834999, "min_metric_value": 4.904246522, "max_metric_value": 5.220257866, "training_avg": 5.062252194, "training_stddev": 0.05266855738, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_FLOAT, the last average value is 5.094. The average for this metric is 5.062.", "is_anomalous": false}], "result_description": "In column NULL_COUNT_FLOAT, the last average value is 5.094. The average for this metric is 5.062."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "zero_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('zero_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_INT, the last zero_count value is 204. The average for this metric is 342.5.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('zero_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Zero Count", "metrics": [{"value": 351.0, "average": 357.0, "min_value": 331.544155877, "max_value": 382.455844123, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "20cb4efcabe6c8e0b0d50ed686694741", "metric_id": "7b8316c794dc50b8eb46d46934774ce9", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_count", "anomaly_score": -0.7071067812, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 351.0, "min_metric_value": 331.544155877, "max_metric_value": 382.455844123, "training_avg": 357.0, "training_stddev": 8.485281374, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_count value is 351. The average for this metric is 357.", "is_anomalous": false}, {"value": 374.0, "average": 362.666666667, "min_value": 328.155798813, "max_value": 397.17753452, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "d903c5f827f163fbacd514c511f4b424", "metric_id": "016d67a0a50a9c98af1d802ef5e371d9", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_count", "anomaly_score": 0.9851968993, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 374.0, "min_metric_value": 328.155798813, "max_metric_value": 397.17753452, "training_avg": 362.666666667, "training_stddev": 11.503622618, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_count value is 374. The average for this metric is 362.667.", "is_anomalous": false}, {"value": 370.0, "average": 364.5, "min_value": 334.251033075, "max_value": 394.748966925, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "c3f9380a5225615484f822231d691d49", "metric_id": "df22a8894c5a86f3e789ad92bafbb772", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_count", "anomaly_score": 0.545473174, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 370.0, "min_metric_value": 334.251033075, "max_metric_value": 394.748966925, "training_avg": 364.5, "training_stddev": 10.082988975, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_count value is 370. The average for this metric is 364.5.", "is_anomalous": false}, {"value": 370.0, "average": 365.6, "min_value": 338.384195768, "max_value": 392.815804232, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "bf3eee081533f62eec0fc644070e3fdc", "metric_id": "56a97c52cfc8f2042a51bba401bcc3ac", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_count", "anomaly_score": 0.4850123071, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 370.0, "min_metric_value": 338.384195768, "max_metric_value": 392.815804232, "training_avg": 365.6, "training_stddev": 9.071934744, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_count value is 370. The average for this metric is 365.6.", "is_anomalous": false}, {"value": 378.0, "average": 367.666666667, "min_value": 338.975204522, "max_value": 396.358128811, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "8122696a54339ebaa9f7bf986e3ff38d", "metric_id": "aca56312041e507e6d48617b6bec8f5e", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_count", "anomaly_score": 1.080460795, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 378.0, "min_metric_value": 338.975204522, "max_metric_value": 396.358128811, "training_avg": 367.666666667, "training_stddev": 9.563820715, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_count value is 378. The average for this metric is 367.667.", "is_anomalous": false}, {"value": 372.0, "average": 368.285714286, "min_value": 341.637208565, "max_value": 394.934220006, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "0c384033d3e618f7af5de36f6b7169e1", "metric_id": "04f7b28412585ade5ab4070a1b52172f", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_count", "anomaly_score": 0.4181419123, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 372.0, "min_metric_value": 341.637208565, "max_metric_value": 394.934220006, "training_avg": 368.285714286, "training_stddev": 8.88283524, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_count value is 372. The average for this metric is 368.286.", "is_anomalous": false}, {"value": 331.0, "average": 363.625, "min_value": 317.012808155, "max_value": 410.237191845, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "d362a7c2ef90273e45a455cf79d0c176", "metric_id": "44d9eb2e0b3eea76caf34db66d2cf871", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_count", "anomaly_score": -2.09977253, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 331.0, "min_metric_value": 317.012808155, "max_metric_value": 410.237191845, "training_avg": 363.625, "training_stddev": 15.537397282, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_count value is 331. The average for this metric is 363.625.", "is_anomalous": false}, {"value": 378.0, "average": 365.222222222, "min_value": 319.311983713, "max_value": 411.132460732, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "ab8b567bba81f2a266e31e92abe809ce", "metric_id": "4f29f256fa678f25281cbe69cb2b9192", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_count", "anomaly_score": 0.8349626266, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 378.0, "min_metric_value": 319.311983713, "max_metric_value": 411.132460732, "training_avg": 365.222222222, "training_stddev": 15.303412837, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_count value is 378. The average for this metric is 365.222.", "is_anomalous": false}, {"value": 366.0, "average": 365.3, "min_value": 322.009123363, "max_value": 408.590876637, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "f8bef5f294c212bdd1a8f01451b8d022", "metric_id": "68bde68121dd22ced886ba5d667414a0", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_count", "anomaly_score": 0.04850906619, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 366.0, "min_metric_value": 322.009123363, "max_metric_value": 408.590876637, "training_avg": 365.3, "training_stddev": 14.430292212, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_count value is 366. The average for this metric is 365.3.", "is_anomalous": false}, {"value": 369.0, "average": 365.636363636, "min_value": 324.430891802, "max_value": 406.841835471, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "51fcd07bdfb6e96ef0afac0597863c18", "metric_id": "2f8d0260aedc40b43da0349d12c69b51", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_count", "anomaly_score": 0.2448924534, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 369.0, "min_metric_value": 324.430891802, "max_metric_value": 406.841835471, "training_avg": 365.636363636, "training_stddev": 13.735157278, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_count value is 369. The average for this metric is 365.636.", "is_anomalous": false}, {"value": 347.0, "average": 364.083333333, "min_value": 321.609544625, "max_value": 406.557122042, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "7c8ab161fe9dbfccaf3dcbcedb0a2072", "metric_id": "124f59d244aba7415186ae5bcf7775f0", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_count", "anomaly_score": -1.206626523, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 347.0, "min_metric_value": 321.609544625, "max_metric_value": 406.557122042, "training_avg": 364.083333333, "training_stddev": 14.15792957, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_count value is 347. The average for this metric is 364.083.", "is_anomalous": false}, {"value": 352.0, "average": 363.153846154, "min_value": 321.263880401, "max_value": 405.043811907, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "077156adb3aff4096f3dda93aa0c9f53", "metric_id": "fbf9db69f772406bfd148617708df266", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_count", "anomaly_score": -0.7987960329, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 352.0, "min_metric_value": 321.263880401, "max_metric_value": 405.043811907, "training_avg": 363.153846154, "training_stddev": 13.963321918, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_count value is 352. The average for this metric is 363.154.", "is_anomalous": false}, {"value": 349.0, "average": 362.142857143, "min_value": 320.326933997, "max_value": 403.958780289, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "05a997f30d6df046a94a7188be3388a3", "metric_id": "8d39821b1b9c8f12294ae0a444144665", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_count", "anomaly_score": -0.9429080709, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 349.0, "min_metric_value": 320.326933997, "max_metric_value": 403.958780289, "training_avg": 362.142857143, "training_stddev": 13.938641049, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_count value is 349. The average for this metric is 362.143.", "is_anomalous": false}, {"value": 344.0, "average": 360.933333333, "min_value": 318.258148172, "max_value": 403.608518495, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "0c1252e3316ebecdda9ea076638863c4", "metric_id": "c8ee22d9e4b6a2b7e9c46dd8a557ec4a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_count", "anomaly_score": -1.190387337, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 344.0, "min_metric_value": 318.258148172, "max_metric_value": 403.608518495, "training_avg": 360.933333333, "training_stddev": 14.225061721, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_count value is 344. The average for this metric is 360.933.", "is_anomalous": false}, {"value": 347.0, "average": 360.0625, "min_value": 317.530600207, "max_value": 402.594399793, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "a966d4aa7f4637b14e3e758a31b781f0", "metric_id": "278a435ef676296ae1fd6308f0216770", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_count", "anomaly_score": -0.9213672606, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 347.0, "min_metric_value": 317.530600207, "max_metric_value": 402.594399793, "training_avg": 360.0625, "training_stddev": 14.177299931, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_count value is 347. The average for this metric is 360.063.", "is_anomalous": false}, {"value": 358.0, "average": 359.941176471, "min_value": 318.732507337, "max_value": 401.149845604, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "fe2e8c9af7cdd301b9c95c2508f05f67", "metric_id": "670389fad86f233ca5449cc6149b5c7d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_count", "anomaly_score": -0.1413180657, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 358.0, "min_metric_value": 318.732507337, "max_metric_value": 401.149845604, "training_avg": 359.941176471, "training_stddev": 13.736223045, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_count value is 358. The average for this metric is 359.941.", "is_anomalous": false}, {"value": 345.0, "average": 359.111111111, "min_value": 317.760387382, "max_value": 400.46183484, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "76dbd3a7f12e8ad395f7c9d2472fa840", "metric_id": "4eea4d19ce2cd196c48f043aa11306ed", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_count", "anomaly_score": -1.023762815, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 345.0, "min_metric_value": 317.760387382, "max_metric_value": 400.46183484, "training_avg": 359.111111111, "training_stddev": 13.783574576, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_count value is 345. The average for this metric is 359.111.", "is_anomalous": false}, {"value": 365.0, "average": 359.421052632, "min_value": 319.031502133, "max_value": 399.81060313, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "76f6672b44d5f6c037b2fa917ee555fb", "metric_id": "f25603760c4c3f3fa0c657ffa5b97633", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_count", "anomaly_score": 0.4143854512, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 365.0, "min_metric_value": 319.031502133, "max_metric_value": 399.81060313, "training_avg": 359.421052632, "training_stddev": 13.4631835, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_count value is 365. The average for this metric is 359.421.", "is_anomalous": false}, {"value": 329.0, "average": 357.9, "min_value": 313.60660023, "max_value": 402.19339977, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "a1adfa60a5b05fa9427d8d62873fdc44", "metric_id": "849f2b8bed8cdc625aae530a89c195d5", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_count", "anomaly_score": -1.957402242, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 329.0, "min_metric_value": 313.60660023, "max_metric_value": 402.19339977, "training_avg": 357.9, "training_stddev": 14.76446659, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_count value is 329. The average for this metric is 357.9.", "is_anomalous": false}, {"value": 374.0, "average": 358.666666667, "min_value": 314.226819682, "max_value": 403.106513651, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "756000f1a7594783af836b150a69d6be", "metric_id": "c61ea7ceb980ed9b35a087fb04a0ab55", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_count", "anomaly_score": 1.035107074, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 374.0, "min_metric_value": 314.226819682, "max_metric_value": 403.106513651, "training_avg": 358.666666667, "training_stddev": 14.813282328, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_count value is 374. The average for this metric is 358.667.", "is_anomalous": false}, {"value": 347.0, "average": 358.136363636, "min_value": 314.130239505, "max_value": 402.142487767, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "8a5b4767096272a2c252fff601ecf281", "metric_id": "44828c5cf4aee48c99126869d35bcacd", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_count", "anomaly_score": -0.7591918527, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 347.0, "min_metric_value": 314.130239505, "max_metric_value": 402.142487767, "training_avg": 358.136363636, "training_stddev": 14.668708044, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_count value is 347. The average for this metric is 358.136.", "is_anomalous": false}, {"value": 344.0, "average": 357.52173913, "min_value": 313.627417602, "max_value": 401.416060659, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "31fd9d28fb188a2fd1e05fcd807d6b39", "metric_id": "9649379cfe77146f643a62962b1c0d3f", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_count", "anomaly_score": -0.9241563824, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 344.0, "min_metric_value": 313.627417602, "max_metric_value": 401.416060659, "training_avg": 357.52173913, "training_stddev": 14.63144051, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_count value is 344. The average for this metric is 357.522.", "is_anomalous": false}, {"value": 344.0, "average": 356.958333333, "min_value": 313.237566703, "max_value": 400.679099964, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "2864e87aa223634754585581963edf71", "metric_id": "a01212d6e07442c78084050cce058cdc", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_count", "anomaly_score": -0.8891655613, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 344.0, "min_metric_value": 313.237566703, "max_metric_value": 400.679099964, "training_avg": 356.958333333, "training_stddev": 14.573588877, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_count value is 344. The average for this metric is 356.958.", "is_anomalous": false}, {"value": 364.0, "average": 357.24, "min_value": 314.231744978, "max_value": 400.248255022, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "a5f2e797fd24e6c9d93ece9acf343134", "metric_id": "2096608b81b42a66e6011faa4be08cdd", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_count", "anomaly_score": 0.4715373825, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 364.0, "min_metric_value": 314.231744978, "max_metric_value": 400.248255022, "training_avg": 357.24, "training_stddev": 14.336085007, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_count value is 364. The average for this metric is 357.24.", "is_anomalous": false}, {"value": 350.0, "average": 356.961538462, "min_value": 314.60748135, "max_value": 399.315595574, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "929a441851bbbd32925d8c6e0d441e62", "metric_id": "12d6b2943961688aa872f48b205fae92", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_count", "anomaly_score": -0.493095982, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 350.0, "min_metric_value": 314.60748135, "max_metric_value": 399.315595574, "training_avg": 356.961538462, "training_stddev": 14.118019037, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_count value is 350. The average for this metric is 356.962.", "is_anomalous": false}, {"value": 374.0, "average": 357.592592593, "min_value": 314.911905794, "max_value": 400.273279392, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "e70c6fad4fb1aa6afd8460698f23fb72", "metric_id": "2d0ea96c16f75ea7fecae6d8ae50a952", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_count", "anomaly_score": 1.153266873, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 374.0, "min_metric_value": 314.911905794, "max_metric_value": 400.273279392, "training_avg": 357.592592593, "training_stddev": 14.2268956, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_count value is 374. The average for this metric is 357.593.", "is_anomalous": false}, {"value": 361.0, "average": 357.714285714, "min_value": 315.786910863, "max_value": 399.641660565, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "e6eab549600b421c33b257f15ecf3663", "metric_id": "8beb2ce0054fb711bde4c521350c3585", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_count", "anomaly_score": 0.2351004062, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 361.0, "min_metric_value": 315.786910863, "max_metric_value": 399.641660565, "training_avg": 357.714285714, "training_stddev": 13.975791617, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_count value is 361. The average for this metric is 357.714.", "is_anomalous": false}, {"value": 55.0, "average": 347.275862069, "min_value": 315.786910863, "max_value": 399.641660565, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "974123cb7a54982eb88f752badef39f6", "metric_id": "ff24349bef8f22d8e24c2d5799356546", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_count", "anomaly_score": -5.051109772, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 55.0, "min_metric_value": 173.684784911, "max_metric_value": 520.866939227, "training_avg": 347.275862069, "training_stddev": 57.863692386, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_count value is 55. The average for this metric is 347.276.", "is_anomalous": true}, {"value": 204.0, "average": 342.5, "min_value": 154.741764036, "max_value": 530.258235964, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "479b31389095f3f3ac8c39d0ebdfeadd", "metric_id": "3d1649cb74aa25a39b52a653f0df8176", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_count", "anomaly_score": -2.212952193, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 204.0, "min_metric_value": 154.741764036, "max_metric_value": 530.258235964, "training_avg": 342.5, "training_stddev": 62.586078655, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_count value is 204. The average for this metric is 342.5.", "is_anomalous": false}], "result_description": "In column NULL_PERCENT_INT, the last zero_count value is 204. The average for this metric is 342.5."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "zero_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('zero_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_INT, the last zero_count value is 240. The average for this metric is 58.7.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('zero_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Zero Count", "metrics": [{"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "369995802a7afb49d35be0b47347bcc2", "metric_id": "6baabf8cd5a9a28fbcafa69b9f696170", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "5f396fa604c429417e262e6a405c552f", "metric_id": "bbcbaf04d731f718938dc2bef687719f", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "54a09b83507ca19fac426325aa92ccc0", "metric_id": "6447e201f36e75f1f262d22453816934", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "c96570c5873032bb1b02dc493d60fa85", "metric_id": "db44c633981c9d36d592b963a37b577e", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "559902775cb1ecd41b1a8715cb486836", "metric_id": "1b287bbee7d30a75e6b43cdcd63bf59f", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "c29e8ef6405fa0ec9b25443746eba7ff", "metric_id": "16e375317e7e7a6ad98b1fa39f75299c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "8d8a1ada3d55972b55e988825c8f7c80", "metric_id": "97c8c30a820ea18aa4e32f562167dfd0", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "b3ce4b0453a09a2e2d708ff0cb9d759c", "metric_id": "f7beaaf8016372b79a5c27ddc1be09ba", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "837aeb21a4eb37c9cf0de115fc5fe3d4", "metric_id": "514bf85dda5387c6bc8391244996bbdf", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "f0c58f7cb5ce7d35432e660e5ae5d571", "metric_id": "39448d6e79df165178cee47a8d48db56", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "9d4c359b8b3096dd6e9688297c43d790", "metric_id": "74b9a78afa717e5f2a339b51d626f735", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "7a84b2c44cf1bc9af4ef81661299f886", "metric_id": "78102bd75880d3746854770416e37410", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "ae2e267754a702a02163dfd630f6621b", "metric_id": "dff72851a4c30776b06e800711c15f3f", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "083cc328930bc36aab66aceb0821dce9", "metric_id": "f6aa09d2778a957c0cb76ca7b0cb8287", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "173c68114f842159c7324cd99777477c", "metric_id": "b937849845c329dc8f1b35c1f5feec28", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "4e6dc70e254292ca36a4ef6511560797", "metric_id": "46f61a4ab76a48fe06b43e26995e0631", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "a4e39533730dc2b715251a43ded97875", "metric_id": "a84a4bf9c7d9f3c2b9d85a8204fcb552", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "d929b34749bd1afa2eba3803bd6d7258", "metric_id": "8b6ed4d60f32b564f71ccdc3c295b4f3", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "3d911b6677ab0215193aae4a4b4f6739", "metric_id": "388cc75e423a5a695b6d89cff12413ed", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "649df41c844f8a062d8f6a57ce82a44c", "metric_id": "6a6fb38459e704991b46e280b35d631b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "17217d5209ab24a92edd266eacdced54", "metric_id": "efa767c691abf00e8f3d2b9c6676f876", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "0fe6490cdd009840500e462e398acf15", "metric_id": "3d2ab5b16d2e6f2dca73659fae8ff859", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "f3ba49e5f34eeb4c1e2bd79de6ee68f4", "metric_id": "7d627c1476c6c4a6f943d4d08bb2de9c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "18c29ab702dc248358a2a7ccb74737d8", "metric_id": "1f7755f13293f7dd2731eaa8ba9366f8", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "c3bcc0e47cb065d15b07879fc1cbe7f6", "metric_id": "81ac2eb527e3e77241c1773a7544a6b2", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "cbf1f7adfadf6cc76b43bd2fe907efc0", "metric_id": "5a5d7189ea74279ea22181d05ccb84bc", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "29c3241fa9e5176d3c463d32e9226789", "metric_id": "7ea5d2acd3bb63c8ae64b7580050c485", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 9.0, "average": 52.448275862, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "d9f28c9d1858db65ef3bdd25fac9aeae", "metric_id": "95743355d33f104d613acef687a87251", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_count", "anomaly_score": -5.199469469, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 9.0, "min_metric_value": 27.379405208, "max_metric_value": 77.517146516, "training_avg": 52.448275862, "training_stddev": 8.356290218, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_count value is 9. The average for this metric is 52.448.", "is_anomalous": true}, {"value": 240.0, "average": 58.7, "min_value": 27.379405208, "max_value": 77.517146516, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "96dda32210e78292032fb819272d6bce", "metric_id": "299ecded43e14c598f76c7de395b4e01", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_count", "anomaly_score": 5.148695731, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 240.0, "min_metric_value": -46.938404067, "max_metric_value": 164.338404067, "training_avg": 58.7, "training_stddev": 35.212801356, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_count value is 240. The average for this metric is 58.7.", "is_anomalous": true}], "result_description": "In column NULL_COUNT_INT, the last zero_count value is 240. The average for this metric is 58.7."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "max", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('max' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_INT, the last max value is 200. The average for this metric is 200.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('max' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Max", "metrics": [{"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "0a904eabdac66b56a8f962dcbc2a7490", "metric_id": "eb9d559376f9ae11319943066a18dcfd", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "1bffa9d00d864d41c220880af6b13cc6", "metric_id": "dff8ede40ed1ad09c480f0ea6efe1072", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "53fee07cda880419edd7c49f77ae3220", "metric_id": "8ff962b11397bdcfa68105fffbca933b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "69a58753ee81a7408013e04f0dd35852", "metric_id": "eb9331c6fa575c1aa5927c458a78668a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "b7d98a017ed77b93d9178fbffe163948", "metric_id": "753e0a3e311b6e55c056726405090b98", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "140eca96933d09c57dcc207cbea2dcb3", "metric_id": "a51cc8ee823f596394e5b2d68ed36ef2", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "ef7da60ed1099581d37ce8956fa08560", "metric_id": "189a6f67a265a84213fa785721050565", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "f92f479af3fea5085dca4faab02fc7c1", "metric_id": "faf7aaeb3b710af670dec9a4f2745bfa", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "bb1d5696035a52f3ae74a3beb165f0b5", "metric_id": "46b639e26fc17294e9f4a334a9f9387d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "7e6761531c4b51e7c3fb510490b9a2b6", "metric_id": "6e97afd521768c955e5f467a622eb5eb", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "8d6d6e18d27ee400be301f4d802de95e", "metric_id": "75a52e1186d47d5664a898756bf25eb8", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "0f17b219288c38a1c0fca0a1b5b8780b", "metric_id": "53e6ba23b9b5908ee4580e5dba6c1fcd", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "b89ae54b9186b1c2a2a864994fefc46f", "metric_id": "60961e55ea3517703e702bd60ccc0f6c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "b94419d5f5252ac0ccba9ca085111f80", "metric_id": "e075b29ece5b58d48ed61abf57ee386f", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "728cf519aac703dea36f8c44aaddf2da", "metric_id": "f679aed030b8ab17f537825e80e1b360", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "be750cfe76a3a9d7da11b40562aae6ad", "metric_id": "560d09a18e93bf0f41cdefce00fbd2ff", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "f97a620e2a1229bb24b476044c1ca1cc", "metric_id": "835883b0f8551cce0881452a55e1b8ba", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "eb1c598fd69ca18fb11398c2c0ed2438", "metric_id": "7482a190bc6dbf6d4a1cf8ebe18cc76d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "49cef6ef2549f0730953ad10ef8df129", "metric_id": "e908ff3b1d5a69878c491a6a9a4c99f8", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "3c8d544b08eaae35442409eda20667bf", "metric_id": "ff31a8ebba707ca272578c2879dc2611", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "25ed00339baabe3802f23eb8c20250d4", "metric_id": "b7b94a092c4e311c21360542dea12027", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "5b2203075d602204a46d067492e6c82e", "metric_id": "99fee204464a3ce7c3544da60b6368ec", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "e06e473a75bba39f2545b38e6c5b02d7", "metric_id": "1f718a36a2f3ee2396323aa50966d031", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "46d2e1618fba6b1a36fb0100b7060fa5", "metric_id": "d482792680f1f719b2c5ce5d45a841cb", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "315f3493a2307671be58dba9a7a71da5", "metric_id": "5a9bb4bda098c824eb6d22cc765221cf", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "9818e8f4684d505e4d68cdaf209dde4a", "metric_id": "2042a084323b4f5ce4bf22932577853d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "2e092c11bc85c8d3df999afc6fa70f55", "metric_id": "31f3b3dcaa7a776ae5b1cdbfd06cffc8", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "81076c390d9115e8151f75be510aaeea", "metric_id": "b8d7ce7b82e4e26e0685147e6f38312e", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "16e20140485864142924c9e7989c8c89", "metric_id": "82676052ae2d505e1bbdf763fccadc22", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}], "result_description": "In column NULL_PERCENT_INT, the last max value is 200. The average for this metric is 200."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "variance", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('variance' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_INT, the last variance value is 901.987. The average for this metric is 851.775.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('variance' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Variance", "metrics": [{"value": 826.40270581, "average": 835.835288797, "min_value": 795.816228429, "max_value": 875.854349165, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "8c74a40f61e5a942d9e7b808c9d139ed", "metric_id": "d3c6e6b754233f636d87b4aba7e60427", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "variance", "anomaly_score": -0.7071067812, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 826.40270581, "min_metric_value": 795.816228429, "max_metric_value": 875.854349165, "training_avg": 835.835288797, "training_stddev": 13.339686789, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last variance value is 826.403. The average for this metric is 835.835.", "is_anomalous": false}, {"value": 818.05149332, "average": 829.907356971, "min_value": 788.079701752, "max_value": 871.735012191, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "108c383a8e2940d94c4519666422716a", "metric_id": "7f6c75c202a8450ac7df477112c5c441", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "variance", "anomaly_score": -0.8503367155, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 818.05149332, "min_metric_value": 788.079701752, "max_metric_value": 871.735012191, "training_avg": 829.907356971, "training_stddev": 13.94255174, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last variance value is 818.051. The average for this metric is 829.907.", "is_anomalous": false}, {"value": 811.749120858, "average": 825.367797943, "min_value": 781.684369091, "max_value": 869.051226795, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "a18059f6f12cff5c7454eb16525c4537", "metric_id": "c9bddb00b2a112cb67a427fb7ec34738", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "variance", "anomaly_score": -0.9352752824, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 811.749120858, "min_metric_value": 781.684369091, "max_metric_value": 869.051226795, "training_avg": 825.367797943, "training_stddev": 14.561142951, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last variance value is 811.749. The average for this metric is 825.368.", "is_anomalous": false}, {"value": 871.528615541, "average": 834.599961463, "min_value": 762.028207911, "max_value": 907.171715015, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "f789610f6c81bf39a1cebf98437a4317", "metric_id": "4b3f543c21f3c130a2dc102ed4605e8e", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "variance", "anomaly_score": 1.526571384, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 871.528615541, "min_metric_value": 762.028207911, "max_metric_value": 907.171715015, "training_avg": 834.599961463, "training_stddev": 24.190584517, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last variance value is 871.529. The average for this metric is 834.6.", "is_anomalous": false}, {"value": 912.207506748, "average": 847.534552344, "min_value": 732.435794059, "max_value": 962.633310628, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "131f02112e8db47f0754a27cf9af2771", "metric_id": "2d3513c10d0ea5f2eb0d3c6903f424ec", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "variance", "anomaly_score": 1.685672948, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 912.207506748, "min_metric_value": 732.435794059, "max_metric_value": 962.633310628, "training_avg": 847.534552344, "training_stddev": 38.366252762, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last variance value is 912.208. The average for this metric is 847.535.", "is_anomalous": false}, {"value": 829.068459129, "average": 844.896539027, "min_value": 737.760203779, "max_value": 952.032874275, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "3d9084a9b9ea6a3af613222f1d40e49e", "metric_id": "088134efc8c96739ce4eb9feadfd0f5a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "variance", "anomaly_score": -0.443213216, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 829.068459129, "min_metric_value": 737.760203779, "max_metric_value": 952.032874275, "training_avg": 844.896539027, "training_stddev": 35.712111749, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last variance value is 829.068. The average for this metric is 844.897.", "is_anomalous": false}, {"value": 855.813980298, "average": 846.261219186, "min_value": 746.398605448, "max_value": 946.123832924, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "520b1873cf9613f2a092afd9753d6359", "metric_id": "14ed881f05cd6a0359d0e47bc9ac2673", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "variance", "anomaly_score": 0.2869771005, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 855.813980298, "min_metric_value": 746.398605448, "max_metric_value": 946.123832924, "training_avg": 846.261219186, "training_stddev": 33.287537913, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last variance value is 855.814. The average for this metric is 846.261.", "is_anomalous": false}, {"value": 831.44167456, "average": 844.614603116, "min_value": 750.033458998, "max_value": 939.195747235, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "7fe0d907ac8ebd881f83b5a4ecedbfb7", "metric_id": "c1d130a89b8c64f0a080b5f62b2b9561", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "variance", "anomaly_score": -0.4178294314, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 831.44167456, "min_metric_value": 750.033458998, "max_metric_value": 939.195747235, "training_avg": 844.614603116, "training_stddev": 31.527048039, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last variance value is 831.442. The average for this metric is 844.615.", "is_anomalous": false}, {"value": 863.144331999, "average": 846.467576005, "min_value": 755.579436889, "max_value": 937.35571512, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "f891af9d863474f34348c9fc1340c375", "metric_id": "c73db55091b7294b2a1d967220837484", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "variance", "anomaly_score": 0.5504598121, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 863.144331999, "min_metric_value": 755.579436889, "max_metric_value": 937.35571512, "training_avg": 846.467576005, "training_stddev": 30.296046372, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last variance value is 863.144. The average for this metric is 846.468.", "is_anomalous": false}, {"value": 835.237109362, "average": 845.446624492, "min_value": 758.62623342, "max_value": 932.267015563, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "f3ec21f5a0e88e42040dc7ade46c0ddb", "metric_id": "6a7b1d6661fe69ce04eb3557ffa4fb54", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "variance", "anomaly_score": -0.3527805509, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 835.237109362, "min_metric_value": 758.62623342, "max_metric_value": 932.267015563, "training_avg": 845.446624492, "training_stddev": 28.940130357, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last variance value is 835.237. The average for this metric is 845.447.", "is_anomalous": false}, {"value": 823.989503052, "average": 843.658531038, "min_value": 758.818483913, "max_value": 928.498578163, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "adcfd4d1fd16a337783cdbf4dde6dd30", "metric_id": "51259219b4377767390b843e11e0e489", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "variance", "anomaly_score": -0.6955097971, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 823.989503052, "min_metric_value": 758.818483913, "max_metric_value": 928.498578163, "training_avg": 843.658531038, "training_stddev": 28.280015708, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last variance value is 823.99. The average for this metric is 843.659.", "is_anomalous": false}, {"value": 831.925064527, "average": 842.755956691, "min_value": 760.943200579, "max_value": 924.568712803, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "d90fcc6533ea57cedc9ec6aaab7be454", "metric_id": "78118af5853eea846b2c1c79720d6dfb", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "variance", "anomaly_score": -0.39715905, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 831.925064527, "min_metric_value": 760.943200579, "max_metric_value": 924.568712803, "training_avg": 842.755956691, "training_stddev": 27.270918704, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last variance value is 831.925. The average for this metric is 842.756.", "is_anomalous": false}, {"value": 853.624551914, "average": 843.532284921, "min_value": 764.447557408, "max_value": 922.617012435, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "426da233aabfc30e2a1ea105a5e16f7b", "metric_id": "65320150f170b88cb7fe5b4428eb0a36", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "variance", "anomaly_score": 0.3828400493, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 853.624551914, "min_metric_value": 764.447557408, "max_metric_value": 922.617012435, "training_avg": 843.532284921, "training_stddev": 26.361575838, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last variance value is 853.625. The average for this metric is 843.532.", "is_anomalous": false}, {"value": 856.524545429, "average": 844.398435622, "min_value": 767.528864346, "max_value": 921.268006898, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "2f72fe77fa6f1dc370ff8cd4c54502f0", "metric_id": "f11c44235a1e59ea2da3cd9dce7d8c69", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "variance", "anomaly_score": 0.4732474608, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 856.524545429, "min_metric_value": 767.528864346, "max_metric_value": 921.268006898, "training_avg": 844.398435622, "training_stddev": 25.623190425, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last variance value is 856.525. The average for this metric is 844.398.", "is_anomalous": false}, {"value": 841.269830255, "average": 844.202897787, "min_value": 769.902775969, "max_value": 918.503019604, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "98d975b534b48a582d8196fd2f53f885", "metric_id": "2a03958e29afe094594aa5d88c7a49e3", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "variance", "anomaly_score": -0.1184278354, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 841.269830255, "min_metric_value": 769.902775969, "max_metric_value": 918.503019604, "training_avg": 844.202897787, "training_stddev": 24.766707272, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last variance value is 841.27. The average for this metric is 844.203.", "is_anomalous": false}, {"value": 856.00795025, "average": 844.897312637, "min_value": 772.4455716, "max_value": 917.349053675, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "efd215d2e2860957b725b186c12baaa1", "metric_id": "32d0ea0cf9f8273dd93a3298a1e8516d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "variance", "anomaly_score": 0.4600567545, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 856.00795025, "min_metric_value": 772.4455716, "max_metric_value": 917.349053675, "training_avg": 844.897312637, "training_stddev": 24.150580346, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last variance value is 856.008. The average for this metric is 844.897.", "is_anomalous": false}, {"value": 829.114718965, "average": 844.020501878, "min_value": 772.851546441, "max_value": 915.189457315, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "a72ebd4448fd608bcabeb8dcd3a0a787", "metric_id": "e761323192c86a0f0e2b7d7898d94fa5", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "variance", "anomaly_score": -0.6283266133, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 829.114718965, "min_metric_value": 772.851546441, "max_metric_value": 915.189457315, "training_avg": 844.020501878, "training_stddev": 23.722985146, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last variance value is 829.115. The average for this metric is 844.021.", "is_anomalous": false}, {"value": 854.209237094, "average": 844.5567511, "min_value": 775.038382895, "max_value": 914.075119305, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "b57c94026f2ed446eac1a06dd33ea1ad", "metric_id": "1e0759ef15ec5214a312e4642add74f4", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "variance", "anomaly_score": 0.416543983, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 854.209237094, "min_metric_value": 775.038382895, "max_metric_value": 914.075119305, "training_avg": 844.5567511, "training_stddev": 23.172789402, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last variance value is 854.209. The average for this metric is 844.557.", "is_anomalous": false}, {"value": 883.392946628, "average": 846.498560876, "min_value": 773.992299247, "max_value": 919.004822505, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "72d23fb561711ba19da5c5ef1697f4c3", "metric_id": "734594ed9b4c8ac9655a2e3049b8a273", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "variance", "anomaly_score": 1.526532396, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 883.392946628, "min_metric_value": 773.992299247, "max_metric_value": 919.004822505, "training_avg": 846.498560876, "training_stddev": 24.168753876, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last variance value is 883.393. The average for this metric is 846.499.", "is_anomalous": false}, {"value": 841.953580374, "average": 846.282133233, "min_value": 775.549163471, "max_value": 917.015102995, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "90b210bd6b25c2e925aa2b5fa311d892", "metric_id": "997aa1705adedb3236bde88d62cfedfe", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "variance", "anomaly_score": -0.1835870687, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 841.953580374, "min_metric_value": 775.549163471, "max_metric_value": 917.015102995, "training_avg": 846.282133233, "training_stddev": 23.577656587, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last variance value is 841.954. The average for this metric is 846.282.", "is_anomalous": false}, {"value": 862.06851598, "average": 846.999696085, "min_value": 777.236832567, "max_value": 916.762559604, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "72f7382938c590f6aab2f4b01dd1616c", "metric_id": "66d204ba8fa977b8a523130679dca774", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "variance", "anomaly_score": 0.6480017792, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 862.06851598, "min_metric_value": 777.236832567, "max_metric_value": 916.762559604, "training_avg": 846.999696085, "training_stddev": 23.25428784, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last variance value is 862.069. The average for this metric is 847.", "is_anomalous": false}, {"value": 868.689316869, "average": 847.942723076, "min_value": 778.446525855, "max_value": 917.438920297, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "d17d26e7813da74d935206bdc0321677", "metric_id": "74ba8f0d431bd9c5bb7c090ed1879ca8", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "variance", "anomaly_score": 0.8955854258, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 868.689316869, "min_metric_value": 778.446525855, "max_metric_value": 917.438920297, "training_avg": 847.942723076, "training_stddev": 23.165399074, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last variance value is 868.689. The average for this metric is 847.943.", "is_anomalous": false}, {"value": 847.914500491, "average": 847.941547135, "min_value": 779.972923207, "max_value": 915.910171063, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "9f53e3ed671eee3065bd3c678cd06e01", "metric_id": "acba626e4cd13adccc0078b633fe2ec7", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "variance", "anomaly_score": -0.001193785118, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 847.914500491, "min_metric_value": 779.972923207, "max_metric_value": 915.910171063, "training_avg": 847.941547135, "training_stddev": 22.656207976, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last variance value is 847.915. The average for this metric is 847.942.", "is_anomalous": false}, {"value": 859.007826619, "average": 848.384198314, "min_value": 781.516183212, "max_value": 915.252213417, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "514e386b43451dc3fa4004e386d6a6bf", "metric_id": "9bf9a0ad83a00ef4d9eec0041f745f95", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "variance", "anomaly_score": 0.476623762, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 859.007826619, "min_metric_value": 781.516183212, "max_metric_value": 915.252213417, "training_avg": 848.384198314, "training_stddev": 22.289338367, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last variance value is 859.008. The average for this metric is 848.384.", "is_anomalous": false}, {"value": 830.864324504, "average": 847.710357014, "min_value": 781.387444556, "max_value": 914.033269472, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "fec98bd60a5f2e2fb86ad24fb04478a7", "metric_id": "0e50c3e79eb686ec7dfb547da85ff125", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "variance", "anomaly_score": -0.7620005765, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 830.864324504, "min_metric_value": 781.387444556, "max_metric_value": 914.033269472, "training_avg": 847.710357014, "training_stddev": 22.107637486, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last variance value is 830.864. The average for this metric is 847.71.", "is_anomalous": false}, {"value": 860.121780468, "average": 848.170039364, "min_value": 782.741493194, "max_value": 913.598585534, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "8c8548b2a3f3a7bb14134167112e54b7", "metric_id": "18ff965b267578f4fc6b8a0137ecd34f", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "variance", "anomaly_score": 0.548005808, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 860.121780468, "min_metric_value": 782.741493194, "max_metric_value": 913.598585534, "training_avg": 848.170039364, "training_stddev": 21.80951539, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last variance value is 860.122. The average for this metric is 848.17.", "is_anomalous": false}, {"value": 837.573180456, "average": 847.791580117, "min_value": 783.305633484, "max_value": 912.277526751, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "e9b94e5dc2d5bc9b6048f959fe49c0e9", "metric_id": "5925853d7940dee2f6e6c0c9caa610a9", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "variance", "anomaly_score": -0.475377979, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 837.573180456, "min_metric_value": 783.305633484, "max_metric_value": 912.277526751, "training_avg": 847.791580117, "training_stddev": 21.495315545, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last variance value is 837.573. The average for this metric is 847.792.", "is_anomalous": false}, {"value": 913.106891937, "average": 850.043832249, "min_value": 777.010410808, "max_value": 923.07725369, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "6cf8ec0e679298d302a7f2c692396bf2", "metric_id": "773c5dc34c829de078bd2790071526df", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "variance", "anomaly_score": 2.590446611, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 913.106891937, "min_metric_value": 777.010410808, "max_metric_value": 923.07725369, "training_avg": 850.043832249, "training_stddev": 24.344473814, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last variance value is 913.107. The average for this metric is 850.044.", "is_anomalous": false}, {"value": 901.986732456, "average": 851.775262256, "min_value": 774.578285676, "max_value": 928.972238836, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "bb1aab928b89b5fb0e6a4b4f57f06110", "metric_id": "4ae7007157a1a8bf9b5dc1d350d6cb91", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "variance", "anomaly_score": 1.951299355, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 901.986732456, "min_metric_value": 774.578285676, "max_metric_value": 928.972238836, "training_avg": 851.775262256, "training_stddev": 25.732325527, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last variance value is 901.987. The average for this metric is 851.775.", "is_anomalous": false}], "result_description": "In column NULL_PERCENT_INT, the last variance value is 901.987. The average for this metric is 851.775."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "zero_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('zero_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_FLOAT, the last zero_percent value is 62.333. The average for this metric is 21.854.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('zero_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Zero Percent", "metrics": [{"value": 21.444, "average": 21.6385, "min_value": 20.813306386, "max_value": 22.463693614, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "edf546b621e00f4f6d4b34ba90006f14", "metric_id": "6103e16d4ba2d6573bc8b3616386cb88", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_percent", "anomaly_score": -0.7071067812, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 21.444, "min_metric_value": 20.813306386, "max_metric_value": 22.463693614, "training_avg": 21.6385, "training_stddev": 0.2750645379, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_percent value is 21.444. The average for this metric is 21.639.", "is_anomalous": false}, {"value": 19.278, "average": 20.851666667, "min_value": 16.72173289, "max_value": 24.981600443, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "e2319871c84e07b90aef3473b569c302", "metric_id": "b8f3023ce7eee9f06c34f32b8900d7c5", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_percent", "anomaly_score": -1.143117603, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 19.278, "min_metric_value": 16.72173289, "max_metric_value": 24.981600443, "training_avg": 20.851666667, "training_stddev": 1.376644592, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_percent value is 19.278. The average for this metric is 20.852.", "is_anomalous": false}, {"value": 20.167, "average": 20.6805, "min_value": 17.155499149, "max_value": 24.205500851, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "3d9a99617cacac54a65d6e595d009e3d", "metric_id": "e87c7b71549cb74193a298ed2b1968ed", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_percent", "anomaly_score": -0.4370211711, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 20.167, "min_metric_value": 17.155499149, "max_metric_value": 24.205500851, "training_avg": 20.6805, "training_stddev": 1.175000284, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_percent value is 20.167. The average for this metric is 20.681.", "is_anomalous": false}, {"value": 20.556, "average": 20.6556, "min_value": 17.598293391, "max_value": 23.712906609, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "7588b92cddc6d6eb91cbb4eecffb24ff", "metric_id": "2c0722222975ebe699c3547f046208a1", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_percent", "anomaly_score": -0.09773308282, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 20.556, "min_metric_value": 17.598293391, "max_metric_value": 23.712906609, "training_avg": 20.6556, "training_stddev": 1.019102203, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_percent value is 20.556. The average for this metric is 20.656.", "is_anomalous": false}, {"value": 19.278, "average": 20.426, "min_value": 17.212843981, "max_value": 23.639156019, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "3f7065180680736f3a6d096610e2a380", "metric_id": "d23dc23034f95c3a1e987a77401af9c6", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_percent", "anomaly_score": -1.071843378, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 19.278, "min_metric_value": 17.212843981, "max_metric_value": 23.639156019, "training_avg": 20.426, "training_stddev": 1.071052006, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_percent value is 19.278. The average for this metric is 20.426.", "is_anomalous": false}, {"value": 20.389, "average": 20.420714286, "min_value": 17.487217542, "max_value": 23.354211029, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "deb6fae75150d7c76bfbd87e54ea0c1b", "metric_id": "08b1556e9f04037d0bacd90d8f763556", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_percent", "anomaly_score": -0.03243325815, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 20.389, "min_metric_value": 17.487217542, "max_metric_value": 23.354211029, "training_avg": 20.420714286, "training_stddev": 0.9778322478, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_percent value is 20.389. The average for this metric is 20.421.", "is_anomalous": false}, {"value": 19.056, "average": 20.250125, "min_value": 17.17257556, "max_value": 23.32767444, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "7e99f1399e4e54765d80651028bb5a6e", "metric_id": "663db3deab1c63b7a6e0ded154472404", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_percent", "anomaly_score": -1.164034915, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 19.056, "min_metric_value": 17.17257556, "max_metric_value": 23.32767444, "training_avg": 20.250125, "training_stddev": 1.025849813, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_percent value is 19.056. The average for this metric is 20.25.", "is_anomalous": false}, {"value": 21.111, "average": 20.345777778, "min_value": 17.341031116, "max_value": 23.350524439, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "e02b507ffb29c98df064c094fc7c1a73", "metric_id": "e5c44a74550e4ec04622c3ecbb5d9adb", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_percent", "anomaly_score": 0.7640133846, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 21.111, "min_metric_value": 17.341031116, "max_metric_value": 23.350524439, "training_avg": 20.345777778, "training_stddev": 1.001582221, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_percent value is 21.111. The average for this metric is 20.346.", "is_anomalous": false}, {"value": 21.833, "average": 20.4945, "min_value": 17.329696294, "max_value": 23.659303706, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "1186525cba04d780d935c145a66b8c87", "metric_id": "2e7ecc08a18b29c313727282bdc45798", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_percent", "anomaly_score": 1.26879907, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 21.833, "min_metric_value": 17.329696294, "max_metric_value": 23.659303706, "training_avg": 20.4945, "training_stddev": 1.054934569, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_percent value is 21.833. The average for this metric is 20.495.", "is_anomalous": false}, {"value": 21.0, "average": 20.540454545, "min_value": 17.503440477, "max_value": 23.577468614, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "1a1e5c89e8eaad945b4529edebb9d5c7", "metric_id": "9876c8de1e9604ead07d266db4458405", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_percent", "anomaly_score": 0.4539446747, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 21.0, "min_metric_value": 17.503440477, "max_metric_value": 23.577468614, "training_avg": 20.540454545, "training_stddev": 1.012338023, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_percent value is 21. The average for this metric is 20.54.", "is_anomalous": false}, {"value": 21.111, "average": 20.588, "min_value": 17.650466991, "max_value": 23.525533009, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "341d8eb696e27e0b2ef21e04cd9b2691", "metric_id": "05aa394225fb340a9a7ed2f3235453ef", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_percent", "anomaly_score": 0.5341216575, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 21.111, "min_metric_value": 17.650466991, "max_metric_value": 23.525533009, "training_avg": 20.588, "training_stddev": 0.9791776698, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_percent value is 21.111. The average for this metric is 20.588.", "is_anomalous": false}, {"value": 20.111, "average": 20.551307692, "min_value": 17.710968174, "max_value": 23.39164721, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "02e1917ea28d56c95d003a2c47705402", "metric_id": "7c47cbdc73765e7bf9860e9294ad0498", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_percent", "anomaly_score": -0.4650581624, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 20.111, "min_metric_value": 17.710968174, "max_metric_value": 23.39164721, "training_avg": 20.551307692, "training_stddev": 0.9467798393, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_percent value is 20.111. The average for this metric is 20.551.", "is_anomalous": false}, {"value": 20.222, "average": 20.527785714, "min_value": 17.78613235, "max_value": 23.269439079, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "0b5e927b948846eaed7bd4ba0a093e17", "metric_id": "62cb6f5e55687001f3e8acc468efffa1", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_percent", "anomaly_score": -0.3345999734, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 20.222, "min_metric_value": 17.78613235, "max_metric_value": 23.269439079, "training_avg": 20.527785714, "training_stddev": 0.9138844548, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_percent value is 20.222. The average for this metric is 20.528.", "is_anomalous": false}, {"value": 21.278, "average": 20.5778, "min_value": 17.872721158, "max_value": 23.282878842, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "080d585ff2913faa16458e57670c9ef3", "metric_id": "afdcedc667bf4e432ac273cef898cf2e", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_percent", "anomaly_score": 0.7765392887, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 21.278, "min_metric_value": 17.872721158, "max_metric_value": 23.282878842, "training_avg": 20.5778, "training_stddev": 0.9016929474, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_percent value is 21.278. The average for this metric is 20.578.", "is_anomalous": false}, {"value": 19.611, "average": 20.517375, "min_value": 17.805292507, "max_value": 23.229457493, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "97fe81b5df3059ecff30cba47b47cef5", "metric_id": "0205df610ece93082a82e311d59c36a8", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_percent", "anomaly_score": -1.002596716, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 19.611, "min_metric_value": 17.805292507, "max_metric_value": 23.229457493, "training_avg": 20.517375, "training_stddev": 0.9040274977, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_percent value is 19.611. The average for this metric is 20.517.", "is_anomalous": false}, {"value": 18.944, "average": 20.424823529, "min_value": 17.560169447, "max_value": 23.289477612, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "97a3ada139e4d16ceff04d0f37b53a6c", "metric_id": "1555858ab937c098d450395a4623d772", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_percent", "anomaly_score": -1.550787795, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 18.944, "min_metric_value": 17.560169447, "max_metric_value": 23.289477612, "training_avg": 20.424823529, "training_stddev": 0.9548846943, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_percent value is 18.944. The average for this metric is 20.425.", "is_anomalous": false}, {"value": 20.444, "average": 20.425888889, "min_value": 17.646733135, "max_value": 23.205044643, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "86bd8a7a772556992ebff33f64ba3655", "metric_id": "6a1d0eb8b7304ce341eb241573f41c08", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_percent", "anomaly_score": 0.01955030165, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 20.444, "min_metric_value": 17.646733135, "max_metric_value": 23.205044643, "training_avg": 20.425888889, "training_stddev": 0.9263852514, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_percent value is 20.444. The average for this metric is 20.426.", "is_anomalous": false}, {"value": 19.222, "average": 20.362526316, "min_value": 17.537434217, "max_value": 23.187618415, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "3f1a30bc09a00162681af1eeb53ced60", "metric_id": "d18a9fbed29b072cf824d2a3031e113a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_percent", "anomaly_score": -1.211138904, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 19.222, "min_metric_value": 17.537434217, "max_metric_value": 23.187618415, "training_avg": 20.362526316, "training_stddev": 0.9416973664, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_percent value is 19.222. The average for this metric is 20.363.", "is_anomalous": false}, {"value": 18.333, "average": 20.26105, "min_value": 17.192723947, "max_value": 23.329376053, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "2fa4a6b1ebfae8c9e30f023ad8574ca9", "metric_id": "5dee179401637422a5d9ee5ba19ce31a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_percent", "anomaly_score": -1.885115826, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 18.333, "min_metric_value": 17.192723947, "max_metric_value": 23.329376053, "training_avg": 20.26105, "training_stddev": 1.022775351, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_percent value is 18.333. The average for this metric is 20.261.", "is_anomalous": false}, {"value": 20.056, "average": 20.251285714, "min_value": 17.257640273, "max_value": 23.244931156, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "9b178195a329f0b0b7a08adf0e29c3d1", "metric_id": "93bd5dc8c1d1f61a02be401eb44f7b06", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_percent", "anomaly_score": -0.1957002438, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 20.056, "min_metric_value": 17.257640273, "max_metric_value": 23.244931156, "training_avg": 20.251285714, "training_stddev": 0.9978818138, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_percent value is 20.056. The average for this metric is 20.251.", "is_anomalous": false}, {"value": 18.944, "average": 20.191863636, "min_value": 17.153066182, "max_value": 23.230661091, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "f5003994cc63338b8fe52dd4622e511d", "metric_id": "a78d63ef5c3afc6d670b78184fd267f9", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_percent", "anomaly_score": -1.231931698, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 18.944, "min_metric_value": 17.153066182, "max_metric_value": 23.230661091, "training_avg": 20.191863636, "training_stddev": 1.012932485, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_percent value is 18.944. The average for this metric is 20.192.", "is_anomalous": false}, {"value": 19.333, "average": 20.154521739, "min_value": 17.137371777, "max_value": 23.171671701, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "a70334cdc8aac97d981005e6eae80695", "metric_id": "eb78a7340ffc0439032e25a2bad75001", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_percent", "anomaly_score": -0.8168520784, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 19.333, "min_metric_value": 17.137371777, "max_metric_value": 23.171671701, "training_avg": 20.154521739, "training_stddev": 1.005716654, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_percent value is 19.333. The average for this metric is 20.155.", "is_anomalous": false}, {"value": 21.167, "average": 20.196708333, "min_value": 17.181443844, "max_value": 23.211972823, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "bdc47d4a78ae671f4580246a648c58b4", "metric_id": "c6dcc38610b17aff21523f4fbea35aa3", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_percent", "anomaly_score": 0.9653796575, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 21.167, "min_metric_value": 17.181443844, "max_metric_value": 23.211972823, "training_avg": 20.196708333, "training_stddev": 1.005088163, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_percent value is 21.167. The average for this metric is 20.197.", "is_anomalous": false}, {"value": 20.556, "average": 20.21108, "min_value": 17.251440378, "max_value": 23.170719622, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "bf37813ce288528c9fae97869fb72267", "metric_id": "2b8475f788dcb5c330c86795f1ffadbf", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_percent", "anomaly_score": 0.3496236476, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 20.556, "min_metric_value": 17.251440378, "max_metric_value": 23.170719622, "training_avg": 20.21108, "training_stddev": 0.9865465405, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_percent value is 20.556. The average for this metric is 20.211.", "is_anomalous": false}, {"value": 20.056, "average": 20.205115385, "min_value": 17.30383757, "max_value": 23.106393199, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "ca3fce57e7d3c28a919785fc901199e4", "metric_id": "aa555b2770df33797e651477a124bff1", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_percent", "anomaly_score": -0.1541893547, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 20.056, "min_metric_value": 17.30383757, "max_metric_value": 23.106393199, "training_avg": 20.205115385, "training_stddev": 0.9670926047, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_percent value is 20.056. The average for this metric is 20.205.", "is_anomalous": false}, {"value": 20.889, "average": 20.230444444, "min_value": 17.358238725, "max_value": 23.102650163, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "89a1715b1f907d61e05ec4ac2d2f8606", "metric_id": "8a1380087e0e231f839e3918eedd65aa", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_percent", "anomaly_score": 0.6878569504, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 20.889, "min_metric_value": 17.358238725, "max_metric_value": 23.102650163, "training_avg": 20.230444444, "training_stddev": 0.9574019063, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_percent value is 20.889. The average for this metric is 20.23.", "is_anomalous": false}, {"value": 21.722, "average": 20.283714286, "min_value": 17.341075917, "max_value": 23.226352655, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "31f28e6a0aca64ce6fdf67769e2c4d39", "metric_id": "fcd646cc1ffc7d6bb01474d305294688", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_percent", "anomaly_score": 1.4663226, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 21.722, "min_metric_value": 17.341075917, "max_metric_value": 23.226352655, "training_avg": 20.283714286, "training_stddev": 0.9808794564, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_percent value is 21.722. The average for this metric is 20.284.", "is_anomalous": false}, {"value": 25.333, "average": 20.457827586, "min_value": 17.341075917, "max_value": 23.226352655, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "2e2eade6885896f4bee1fd25e0ecf408", "metric_id": "3f6887e427fe4237656ddc49097f79a9", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_percent", "anomaly_score": 3.626783509, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 25.333, "min_metric_value": 16.42518621, "max_metric_value": 24.490468963, "training_avg": 20.457827586, "training_stddev": 1.344213792, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_percent value is 25.333. The average for this metric is 20.458.", "is_anomalous": true}, {"value": 62.333, "average": 21.853666667, "min_value": 16.42518621, "max_value": 24.490468963, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "4fd5020e06e8f92cbf5097496b3e31c6", "metric_id": "fc928d0a5e1ed736415ca0531c93895e", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_percent", "anomaly_score": 5.217361839, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 62.333, "min_metric_value": -1.422081452, "max_metric_value": 45.129414785, "training_avg": 21.853666667, "training_stddev": 7.758582706, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_percent value is 62.333. The average for this metric is 21.854.", "is_anomalous": true}], "result_description": "In column NULL_PERCENT_FLOAT, the last zero_percent value is 62.333. The average for this metric is 21.854."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "variance", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('variance' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_INT, the last variance value is 972.623. The average for this metric is 856.161.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('variance' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Variance", "metrics": [{"value": 847.862686386, "average": 849.021349987, "min_value": 844.10555665, "max_value": 853.937143324, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "a5788f57b8b89552194babfc258dc036", "metric_id": "a4e9beaba05fc3c9dd3986c14522ef96", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "variance", "anomaly_score": -0.7071067812, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 847.862686386, "min_metric_value": 844.10555665, "max_metric_value": 853.937143324, "training_avg": 849.021349987, "training_stddev": 1.638597779, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last variance value is 847.863. The average for this metric is 849.021.", "is_anomalous": false}, {"value": 845.961608195, "average": 848.001436056, "min_value": 841.663567995, "max_value": 854.339304118, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "f1837a6ca699ecd5ad01e3dcd34dd6aa", "metric_id": "e2a21cf9a80ce76abefd47bb4724ec08", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "variance", "anomaly_score": -0.965542912, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 845.961608195, "min_metric_value": 841.663567995, "max_metric_value": 854.339304118, "training_avg": 848.001436056, "training_stddev": 2.112622687, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last variance value is 845.962. The average for this metric is 848.001.", "is_anomalous": false}, {"value": 846.593566301, "average": 847.649468618, "min_value": 842.060303517, "max_value": 853.238633718, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "d491b62b75601acd674cca68577cb6aa", "metric_id": "b577daa7c273982161eb995f15b4f331", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "variance", "anomaly_score": -0.5667585215, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 846.593566301, "min_metric_value": 842.060303517, "max_metric_value": 853.238633718, "training_avg": 847.649468618, "training_stddev": 1.863055033, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last variance value is 846.594. The average for this metric is 847.649.", "is_anomalous": false}, {"value": 845.171935197, "average": 847.153961933, "min_value": 841.282183309, "max_value": 853.025740558, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "07061c3c0447a3d067631a8693c0423c", "metric_id": "9eb719089d5d43c49fde08188b1f869a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "variance", "anomaly_score": -1.012654017, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 845.171935197, "min_metric_value": 841.282183309, "max_metric_value": 853.025740558, "training_avg": 847.153961933, "training_stddev": 1.957259542, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last variance value is 845.172. The average for this metric is 847.154.", "is_anomalous": false}, {"value": 847.159755413, "average": 847.154927513, "min_value": 841.903044258, "max_value": 852.406810768, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "382a451e2e21f9a8ae7d03cee26f7a56", "metric_id": "e7035e0259241f869f5f9b58415083b3", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "variance", "anomaly_score": 0.002757810566, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 847.159755413, "min_metric_value": 841.903044258, "max_metric_value": 852.406810768, "training_avg": 847.154927513, "training_stddev": 1.750627752, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last variance value is 847.16. The average for this metric is 847.155.", "is_anomalous": false}, {"value": 876.355776445, "average": 851.326477361, "min_value": 817.870530153, "max_value": 884.782424569, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "f31db921a901cd62650313e3f30d02e1", "metric_id": "2e7bd1cf56fb9b369ac60c5251295df7", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "variance", "anomaly_score": 2.24438115, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 876.355776445, "min_metric_value": 817.870530153, "max_metric_value": 884.782424569, "training_avg": 851.326477361, "training_stddev": 11.151982403, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last variance value is 876.356. The average for this metric is 851.326.", "is_anomalous": false}, {"value": 853.496657772, "average": 851.597749912, "min_value": 820.538150119, "max_value": 882.657349706, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "25a1b814039df132c1c235e88ad4bcdd", "metric_id": "d9d14cd7e60752ef11fdd2ef3632f179", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "variance", "anomaly_score": 0.1834126524, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 853.496657772, "min_metric_value": 820.538150119, "max_metric_value": 882.657349706, "training_avg": 851.597749912, "training_stddev": 10.353199931, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last variance value is 853.497. The average for this metric is 851.598.", "is_anomalous": false}, {"value": 865.341586336, "average": 853.124842848, "min_value": 820.98445647, "max_value": 885.265229226, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "23eb1336ba89d66c1be43d3af491e26d", "metric_id": "66847dd52e41f406d39d4bfaad58b856", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "variance", "anomaly_score": 1.140317046, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 865.341586336, "min_metric_value": 820.98445647, "max_metric_value": 885.265229226, "training_avg": 853.124842848, "training_stddev": 10.713462126, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last variance value is 865.342. The average for this metric is 853.125.", "is_anomalous": false}, {"value": 849.331932834, "average": 852.745551847, "min_value": 822.230412726, "max_value": 883.260690967, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "297b5dcdebac5c84de1b64c3bea811fd", "metric_id": "084cd1dd95cf308755268ddc5c0faa23", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "variance", "anomaly_score": -0.335599225, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 849.331932834, "min_metric_value": 822.230412726, "max_metric_value": 883.260690967, "training_avg": 852.745551847, "training_stddev": 10.17171304, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last variance value is 849.332. The average for this metric is 852.746.", "is_anomalous": false}, {"value": 832.293270578, "average": 850.88625355, "min_value": 816.530782812, "max_value": 885.241724287, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "1926d809df537718a568a6021b95ae72", "metric_id": "84db693ad5a01cf0eb110c7d588e3afa", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "variance", "anomaly_score": -1.623582728, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 832.293270578, "min_metric_value": 816.530782812, "max_metric_value": 885.241724287, "training_avg": 850.88625355, "training_stddev": 11.451823579, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last variance value is 832.293. The average for this metric is 850.886.", "is_anomalous": false}, {"value": 881.853002688, "average": 853.466815978, "min_value": 811.132395207, "max_value": 895.801236748, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "1a6e234dce9e7c9dc3938871f0558b3a", "metric_id": "67d0513cd9b38a4ddb32645d19172711", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "variance", "anomaly_score": 2.011567859, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 881.853002688, "min_metric_value": 811.132395207, "max_metric_value": 895.801236748, "training_avg": 853.466815978, "training_stddev": 14.11147359, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last variance value is 881.853. The average for this metric is 853.467.", "is_anomalous": false}, {"value": 857.716777111, "average": 853.793736065, "min_value": 813.107651272, "max_value": 894.479820858, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "941b29aaa6e25069af3983ca48cd5971", "metric_id": "19c50be3f87be84cafa097c795987294", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "variance", "anomaly_score": 0.2892665441, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 857.716777111, "min_metric_value": 813.107651272, "max_metric_value": 894.479820858, "training_avg": 853.793736065, "training_stddev": 13.562028264, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last variance value is 857.717. The average for this metric is 853.794.", "is_anomalous": false}, {"value": 833.568478421, "average": 852.349074805, "min_value": 810.0289836, "max_value": 894.66916601, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "dabeae476dcc9c716ac3102751f1f6ab", "metric_id": "84863124d5b2a5d331031fe27f73721a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "variance", "anomaly_score": -1.331324852, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 833.568478421, "min_metric_value": 810.0289836, "max_metric_value": 894.66916601, "training_avg": 852.349074805, "training_stddev": 14.106697068, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last variance value is 833.568. The average for this metric is 852.349.", "is_anomalous": false}, {"value": 829.308811627, "average": 850.81305726, "min_value": 806.298177834, "max_value": 895.327936685, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "2a5e2660b2d8c62259380d14637c54e7", "metric_id": "02d4f06bf699fdb60f97801af6a38d51", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "variance", "anomaly_score": -1.449239844, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 829.308811627, "min_metric_value": 806.298177834, "max_metric_value": 895.327936685, "training_avg": 850.81305726, "training_stddev": 14.838293142, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last variance value is 829.309. The average for this metric is 850.813.", "is_anomalous": false}, {"value": 840.69252126, "average": 850.18052376, "min_value": 806.510354001, "max_value": 893.850693519, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "7fe181b3d8145c0eb61824bed519170c", "metric_id": "fa549c1acbcd6d0639dadf6a300902d6", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "variance", "anomaly_score": -0.6517952107, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 840.69252126, "min_metric_value": 806.510354001, "max_metric_value": 893.850693519, "training_avg": 850.18052376, "training_stddev": 14.556723253, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last variance value is 840.693. The average for this metric is 850.181.", "is_anomalous": false}, {"value": 803.385191859, "average": 847.427857177, "min_value": 793.139750645, "max_value": 901.71596371, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "947fc717e99658b299af9b2a9646495e", "metric_id": "e72bf76f11516a1fce8eeb9ec4721e9a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "variance", "anomaly_score": -2.433829514, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 803.385191859, "min_metric_value": 793.139750645, "max_metric_value": 901.71596371, "training_avg": 847.427857177, "training_stddev": 18.096035511, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last variance value is 803.385. The average for this metric is 847.428.", "is_anomalous": false}, {"value": 852.98630123, "average": 847.736659625, "min_value": 794.923005682, "max_value": 900.550313567, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "2d588161119926e27e5fe14b6e4b38ff", "metric_id": "02c2fd032817e14603745efec91dbf41", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "variance", "anomaly_score": 0.298197978, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 852.98630123, "min_metric_value": 794.923005682, "max_metric_value": 900.550313567, "training_avg": 847.736659625, "training_stddev": 17.604551314, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last variance value is 852.986. The average for this metric is 847.737.", "is_anomalous": false}, {"value": 882.116610049, "average": 849.5461307, "min_value": 793.028808913, "max_value": 906.063452486, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "5c514cf6a395ec1555bc09a3b7e747bb", "metric_id": "1b0125aa674a3e3a3d4968e87d238d49", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "variance", "anomaly_score": 1.728875944, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 882.116610049, "min_metric_value": 793.028808913, "max_metric_value": 906.063452486, "training_avg": 849.5461307, "training_stddev": 18.839107262, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last variance value is 882.117. The average for this metric is 849.546.", "is_anomalous": false}, {"value": 833.291893054, "average": 848.733418817, "min_value": 792.653283494, "max_value": 904.81355414, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "8beb7215006b78af7af2acd4139113a3", "metric_id": "52c724be40386a03e5fdd02ae68785da", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "variance", "anomaly_score": -0.8260425376, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 833.291893054, "min_metric_value": 792.653283494, "max_metric_value": 904.81355414, "training_avg": 848.733418817, "training_stddev": 18.693378441, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last variance value is 833.292. The average for this metric is 848.733.", "is_anomalous": false}, {"value": 858.759814492, "average": 849.21086623, "min_value": 794.158017176, "max_value": 904.263715285, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "9e76cf353db40e951ada2af0d675b30b", "metric_id": "a61d9079dc414bf5b036a25e632164eb", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "variance", "anomaly_score": 0.5203517216, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 858.759814492, "min_metric_value": 794.158017176, "max_metric_value": 904.263715285, "training_avg": 849.21086623, "training_stddev": 18.350949685, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last variance value is 858.76. The average for this metric is 849.211.", "is_anomalous": false}, {"value": 863.621543142, "average": 849.865896999, "min_value": 795.354920677, "max_value": 904.376873321, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "aa503b9428c371408bd02f453953a323", "metric_id": "370fbdfa416ffff724491b1e9e751b9c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "variance", "anomaly_score": 0.7570390628, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 863.621543142, "min_metric_value": 795.354920677, "max_metric_value": 904.376873321, "training_avg": 849.865896999, "training_stddev": 18.170325441, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last variance value is 863.622. The average for this metric is 849.866.", "is_anomalous": false}, {"value": 833.497690013, "average": 849.154235826, "min_value": 794.921235316, "max_value": 903.387236336, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "0f5032c3605a8ac1f2e13a789ed1c7bd", "metric_id": "470209dfc20a5ab1aab24af5d9432e11", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "variance", "anomaly_score": -0.8660711559, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 833.497690013, "min_metric_value": 794.921235316, "max_metric_value": 903.387236336, "training_avg": 849.154235826, "training_stddev": 18.077666837, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last variance value is 833.498. The average for this metric is 849.154.", "is_anomalous": false}, {"value": 840.160278918, "average": 848.779487621, "min_value": 795.453382301, "max_value": 902.105592942, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "737742c86ef2627c08204fe7d8c70570", "metric_id": "1b0314070c504d2cf8bd3904323dea76", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "variance", "anomaly_score": -0.4848962052, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 840.160278918, "min_metric_value": 795.453382301, "max_metric_value": 902.105592942, "training_avg": 848.779487621, "training_stddev": 17.77536844, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last variance value is 840.16. The average for this metric is 848.779.", "is_anomalous": false}, {"value": 874.549228199, "average": 849.810277244, "min_value": 795.365298201, "max_value": 904.255256288, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "cc8d29b0a3c22da48a521e058d5f9798", "metric_id": "cd57cbee9c3c52c9d3b3d3ecd80f726a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "variance", "anomaly_score": 1.3631533, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 874.549228199, "min_metric_value": 795.365298201, "max_metric_value": 904.255256288, "training_avg": 849.810277244, "training_stddev": 18.148326348, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last variance value is 874.549. The average for this metric is 849.81.", "is_anomalous": false}, {"value": 858.822780847, "average": 850.156911998, "min_value": 796.549058692, "max_value": 903.764765304, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "94f741507baa31ca78c747992a9dd10a", "metric_id": "42a1253483e4dac35d9eeb3ea4166618", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "variance", "anomaly_score": 0.4849589182, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 858.822780847, "min_metric_value": 796.549058692, "max_metric_value": 903.764765304, "training_avg": 850.156911998, "training_stddev": 17.869284435, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last variance value is 858.823. The average for this metric is 850.157.", "is_anomalous": false}, {"value": 851.189873866, "average": 850.195169845, "min_value": 797.624961909, "max_value": 902.765377781, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "35160eb17a2d02b5001a3546ccfbe509", "metric_id": "0a47ee868f1877b8b21cee04457a6041", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "variance", "anomaly_score": 0.05676431919, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 851.189873866, "min_metric_value": 797.624961909, "max_metric_value": 902.765377781, "training_avg": 850.195169845, "training_stddev": 17.523402645, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last variance value is 851.19. The average for this metric is 850.195.", "is_anomalous": false}, {"value": 841.189458016, "average": 849.87353728, "min_value": 798.033986863, "max_value": 901.713087697, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "10c05b4eb487aa2f4ffa93c33600d35e", "metric_id": "c23939c800016a691bdad5f13c848522", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "variance", "anomaly_score": -0.5025552417, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 841.189458016, "min_metric_value": 798.033986863, "max_metric_value": 901.713087697, "training_avg": 849.87353728, "training_stddev": 17.279850139, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last variance value is 841.189. The average for this metric is 849.874.", "is_anomalous": false}, {"value": 915.761559426, "average": 852.145538044, "min_value": 798.033986863, "max_value": 901.713087697, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "1d26e47ffbe8f064d9f4f7f8e85a2ad3", "metric_id": "731797886c165df0e90a6227a0305fde", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "variance", "anomaly_score": 3.040986852, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 915.761559426, "min_metric_value": 789.386942419, "max_metric_value": 914.904133668, "training_avg": 852.145538044, "training_stddev": 20.919531875, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last variance value is 915.762. The average for this metric is 852.146.", "is_anomalous": true}, {"value": 972.62259887, "average": 856.161440071, "min_value": 789.386942419, "max_value": 914.904133668, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "50c92f2cccd82ae6991ddaf9c738c372", "metric_id": "d899096000d14abac0e5d4b268a96690", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "variance", "anomaly_score": 3.868393707, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 972.62259887, "min_metric_value": 765.843984603, "max_metric_value": 946.47889554, "training_avg": 856.161440071, "training_stddev": 30.105818489, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last variance value is 972.623. The average for this metric is 856.161.", "is_anomalous": true}], "result_description": "In column NULL_COUNT_INT, the last variance value is 972.623. The average for this metric is 856.161."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "average_length", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average_length' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_STR, the last average_length value is 5. The average for this metric is 5.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average_length' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Average Length", "metrics": [{"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "cc5380d6f8cb2ab7725942c82695e692", "metric_id": "0e9d321f51da48305a7d56e050114ed5", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "2de2ae8e4906cc263bd51d252cd53592", "metric_id": "143753e9863c4c21d979f699d2ac9d06", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "0f54956347938413137430e2762f16ff", "metric_id": "73e08687302ba05d0d0cc7f61c559468", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "279af7c3af3ebffa216d6425180ef677", "metric_id": "70bdbd928109af0fa841cb37069ecb20", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "560270f0c7ff33eace6ca3a7b6e12818", "metric_id": "d5ff58accfc0cd134f64383308d0f97b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "e796cb95d3cbccc8c4fa384c5aedcfbb", "metric_id": "fbc8fd92625d6ca29a1db0c401bbe566", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "77532d425e278d3262a20c32e1b41b2d", "metric_id": "aa998278e8d33b723ff71ae865b3cbb1", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "99f91da2d930ee24b11ff74dea6e6530", "metric_id": "b45887e6ad22df339ea3e949a5b36097", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "1b30188d6418cb65fb562991d107b49c", "metric_id": "dd6a1e497a782619c652c5eb0dffcc15", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "1734aa8494e6f734ecf87b8cd7e08465", "metric_id": "b51c6c9aa86926110967865c2eea3255", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "270f652192ad26cf98c55d3252d484dd", "metric_id": "86ea0e28a9462657edc2cbcb98ba96b1", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "f1bd32c5bab03e39b7785da06d2ef71c", "metric_id": "8747cd00d496ff54847d446bd6d2cb65", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "26dcccd286182d472fd5fac4440ef122", "metric_id": "085aac35295414af504b1d4fac57bd7d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "5947aa89f4225ebcd53e2a77cf296154", "metric_id": "bbfef5d10403900b0ca82a5991ee985d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "52e5fe6a737b264d835e43711eb911c1", "metric_id": "6555c7595ff00fc970f4bf38e352644b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "ee4a131ff88f0cf75c1c204f1253caf6", "metric_id": "541a85208b28deb075fbf907ca5c86c7", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "8df75a919de97d012f596724c374d5a3", "metric_id": "eaca7ee82310524fdd82e30a02738c01", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "7dd7a5f4b740d48f288734146acc304d", "metric_id": "8dd19609800a1da0d417a6ef9ec0b7c3", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "719da5ff0a9e3601860b6536a39f109d", "metric_id": "ec3cdb1b0ecc7fb8d27772c3aa340bb2", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "7223d5f8645337cf507608b3135a1530", "metric_id": "196261f6a10f70f5345b613e9c9c123c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "1001c6b550b73443beb2ecdd544d1a29", "metric_id": "1370ad98d33a627b97dd094b88f41cc5", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "369d86297215d3cc38170778c64b4d98", "metric_id": "2f8c84cc3935e7d0a47edf84d08a7c36", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "b1fa9eeec1b271812061a7fa1b5d93ba", "metric_id": "016aa3acf0452607efee53ef61c93325", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "8f68f57eb89694af5260b764ec4e2246", "metric_id": "ead251eb02720a6dff3fd39107fc5922", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "b6efb8a2120e16079fffbf9d0588f4b0", "metric_id": "8418b1939da81b35e03e282eb066d077", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "974afe40bd35f47984ec9feac882c62a", "metric_id": "ae056039b750095c5c91f74fafa85f37", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "3b7bb7738dc22e2c6e7e9fbab15574b5", "metric_id": "883b2d5be7657903c45853bb299777d3", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "920cb3b26b5d307894af415b42b76611", "metric_id": "ad36835c8823354d98ecf909e6011b73", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "ae138ebf4dcc8c49e5a0f7246e590108", "metric_id": "e5dd877d0f15472ccb4bdf00d2b7c929", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}], "result_description": "In column NULL_COUNT_STR, the last average_length value is 5. The average for this metric is 5."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "missing_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('missing_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_STR, the last missing_count value is 240. The average for this metric is 58.7.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('missing_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Missing Count", "metrics": [{"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "5b8e794c00694956a2c2b6976ff78477", "metric_id": "679e3f86b04cd01d46e609117b7a5fd5", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "0cbf18562dbb6930c1289e8cbf31e42d", "metric_id": "5645c9405d37956a1ebb4114ff30bfb7", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "d47b0596c2e32343f9f30ab943fe3d98", "metric_id": "dadcc8bf2c8f9ed9c1588e84fae276a7", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "e935d556b0de6c7c4a65e31ae7645230", "metric_id": "5885597eb8d8e2ee681cf6832905a219", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "da15161b40968f9c83bf560f24265ae0", "metric_id": "f5681f1557299fa15f7e14f485563c36", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "792ea9da4c37640e8320dc0c5e28f4bb", "metric_id": "4bc86da0fe07e6f2531a375a66ac962f", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "a60576387fb740b61b7d29cc0dfa8cb1", "metric_id": "d829136f8f4c8d6eda2ea49b981a29d1", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "0a3494b8659a5589e089888baf576a63", "metric_id": "e13a0f771797abff17da6f23ad48f0a3", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "0fa5266b0a0fe9c356dcc2e8c9a4c613", "metric_id": "159cba44a7bf780464763f4f9d92afc6", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "65694f0ac8021943a11316ed62130a25", "metric_id": "023101999403be9097796e7b1c608ec1", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "26877b31011986ea01d19e30cd6f1c15", "metric_id": "504782069d22b8dd7eeb9f6ddc7180d8", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "472511803b667a455c68d47ad3ef316e", "metric_id": "b1e66479e094b39d93faa6e1b89ab70e", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "ed57a7c7ab10a883c8440546634400fe", "metric_id": "84761712503939ad98a278d9e14d2e29", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "43faea601567a476bf7df24d952a4ea6", "metric_id": "661fbc890fb3a27b52d9b7b0bdbc7203", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "b29e61d51f4de1ebd2bc2ae7d8de08aa", "metric_id": "587165e94239e9f5675f85e37e409797", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "a600c90067bdbc5aa943853a64367472", "metric_id": "550a494f6a2e89b683d10494364f0ee1", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "e71842f9612e6ee56be9373f6edab39b", "metric_id": "5f53aa675ca34212634dd07c35312926", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "d4494f2d53ebff67092e1c43c80ce3cc", "metric_id": "1a1a2e1c8fb05ad24d3cb1c0da038a85", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "d3a6d96a02444848f4c4e260db370e05", "metric_id": "b8ded3312841043ef5aacd46bdd6bab4", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "8c4790149a8939a7be43772132fc7e2a", "metric_id": "dc5a66b91ae21456de2c364410d4ce8e", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "d9ce86e5a9357d27489219eb93786788", "metric_id": "d4b7e811f0b1ce2dbbbdfa065742182e", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "3eab9783a09f7b55442f7d60ac9c4041", "metric_id": "2fab25fd95cb19077b45f9d9d8a25d52", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "8d21a1a14934b6eba84931aaf4a091d0", "metric_id": "4840c5fff000a93022863e450a54e438", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "89fe3bdde0985be8d81946d17589fa5d", "metric_id": "1eb526c75567e24dd1f75887dc084ed7", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "162e8f832fa0370aeccd9b6b08267515", "metric_id": "e8bac2d27bcb4943ebfe343fa5add4c1", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "0f53bd0defb9058538dd1d5db4b5f369", "metric_id": "fc3e5fd27ff83dce2ea9e19675f6d8c4", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 54.0, "average": 54.0, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "73f7dc7eafa4a1a2a44d714c44b19ef9", "metric_id": "1d02fa03a23a316e7abcf7162a6744d9", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 54.0, "min_metric_value": 54.0, "max_metric_value": 54.0, "training_avg": 54.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_count value is 54. The average for this metric is 54.", "is_anomalous": false}, {"value": 9.0, "average": 52.448275862, "min_value": 54.0, "max_value": 54.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "1891f1812664324756887f9bcd813464", "metric_id": "e4dbe77bef47b48562144489da8067e0", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_count", "anomaly_score": -5.199469469, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 9.0, "min_metric_value": 27.379405208, "max_metric_value": 77.517146516, "training_avg": 52.448275862, "training_stddev": 8.356290218, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_count value is 9. The average for this metric is 52.448.", "is_anomalous": true}, {"value": 240.0, "average": 58.7, "min_value": 27.379405208, "max_value": 77.517146516, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "d1f11f1580fca50165940f87ddb04293", "metric_id": "028d5bdc3b143439a4c8441fb2be80cd", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "missing_count", "anomaly_score": 5.148695731, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 240.0, "min_metric_value": -46.938404067, "max_metric_value": 164.338404067, "training_avg": 58.7, "training_stddev": 35.212801356, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last missing_count value is 240. The average for this metric is 58.7.", "is_anomalous": true}], "result_description": "In column NULL_COUNT_STR, the last missing_count value is 240. The average for this metric is 58.7."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "zero_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('zero_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_INT, the last zero_percent value is 68. The average for this metric is 21.426.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('zero_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Zero Percent", "metrics": [{"value": 19.5, "average": 19.8335, "min_value": 18.418579331, "max_value": 21.248420669, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "b1865e6496b87cb5fa6c72d913678bd1", "metric_id": "f730637b49dc344d2b780d6092504d16", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_percent", "anomaly_score": -0.7071067812, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 19.5, "min_metric_value": 18.418579331, "max_metric_value": 21.248420669, "training_avg": 19.8335, "training_stddev": 0.4716402231, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_percent value is 19.5. The average for this metric is 19.834.", "is_anomalous": false}, {"value": 20.778, "average": 20.148333333, "min_value": 18.230719973, "max_value": 22.065946694, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "f7f6e57003da5d2c5f76759a762fcdb4", "metric_id": "70b9747e3fc214c616623194a88bc2be", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_percent", "anomaly_score": 0.9850786603, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 20.778, "min_metric_value": 18.230719973, "max_metric_value": 22.065946694, "training_avg": 20.148333333, "training_stddev": 0.6392044535, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_percent value is 20.778. The average for this metric is 20.148.", "is_anomalous": false}, {"value": 20.556, "average": 20.25025, "min_value": 18.569349572, "max_value": 21.931150428, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "cedb2f128ce408874ef68a7525c17d9b", "metric_id": "ea7c5c5dadea6368c3225651b5785df9", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_percent", "anomaly_score": 0.54568967, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 20.556, "min_metric_value": 18.569349572, "max_metric_value": 21.931150428, "training_avg": 20.25025, "training_stddev": 0.5603001428, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_percent value is 20.556. The average for this metric is 20.25.", "is_anomalous": false}, {"value": 20.556, "average": 20.3114, "min_value": 18.799004814, "max_value": 21.823795186, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "2854eb0e17558e4f53b9e427dd4f1738", "metric_id": "c20917da56590cb05d7a858c4fca6d75", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_percent", "anomaly_score": 0.4851906476, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 20.556, "min_metric_value": 18.799004814, "max_metric_value": 21.823795186, "training_avg": 20.3114, "training_stddev": 0.5041317288, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_percent value is 20.556. The average for this metric is 20.311.", "is_anomalous": false}, {"value": 21.0, "average": 20.426166667, "min_value": 18.832075609, "max_value": 22.020257724, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "a983e8172faf9580e19e44443c15d5e0", "metric_id": "01510aee2331dfb51f902960477351f6", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_percent", "anomaly_score": 1.079925762, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 21.0, "min_metric_value": 18.832075609, "max_metric_value": 22.020257724, "training_avg": 20.426166667, "training_stddev": 0.5313636859, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_percent value is 21. The average for this metric is 20.426.", "is_anomalous": false}, {"value": 20.667, "average": 20.460571429, "min_value": 18.979971019, "max_value": 21.941171838, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "05f88e900aaf17682277867ead8c225b", "metric_id": "2c357b3c15f6d10e4ed36d2b9f2d7cef", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_percent", "anomaly_score": 0.4182666103, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 20.667, "min_metric_value": 18.979971019, "max_metric_value": 21.941171838, "training_avg": 20.460571429, "training_stddev": 0.4935334697, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_percent value is 20.667. The average for this metric is 20.461.", "is_anomalous": false}, {"value": 18.389, "average": 20.201625, "min_value": 17.611867569, "max_value": 22.791382431, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "3b90d073adbb329bf3f08173ef42c7fe", "metric_id": "1c7b47fa50467584e6ee8c39c38d1464", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_percent", "anomaly_score": -2.099762292, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 18.389, "min_metric_value": 17.611867569, "max_metric_value": 22.791382431, "training_avg": 20.201625, "training_stddev": 0.8632524771, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_percent value is 18.389. The average for this metric is 20.202.", "is_anomalous": false}, {"value": 21.0, "average": 20.290333333, "min_value": 17.739668567, "max_value": 22.8409981, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "46fe11d0207e2272ddf3df8c9f927053", "metric_id": "a27ef2f17f39622121b98309275bb89b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_percent", "anomaly_score": 0.8346843647, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 21.0, "min_metric_value": 17.739668567, "max_metric_value": 22.8409981, "training_avg": 20.290333333, "training_stddev": 0.8502215888, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_percent value is 21. The average for this metric is 20.29.", "is_anomalous": false}, {"value": 20.333, "average": 20.2946, "min_value": 17.889469567, "max_value": 22.699730433, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "eec29133e339d37630870b23a8d191a8", "metric_id": "8c082b1984ec47277b947fbbf91bc3cd", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_percent", "anomaly_score": 0.04789761022, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 20.333, "min_metric_value": 17.889469567, "max_metric_value": 22.699730433, "training_avg": 20.2946, "training_stddev": 0.8017101444, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_percent value is 20.333. The average for this metric is 20.295.", "is_anomalous": false}, {"value": 20.5, "average": 20.313272727, "min_value": 18.024013991, "max_value": 22.602531464, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "d1feace9be31e9241e2ea52c4ccee80e", "metric_id": "fb794e4559844cceb08bffb14a884f33", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_percent", "anomaly_score": 0.2447000897, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 20.5, "min_metric_value": 18.024013991, "max_metric_value": 22.602531464, "training_avg": 20.313272727, "training_stddev": 0.7630862456, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_percent value is 20.5. The average for this metric is 20.313.", "is_anomalous": false}, {"value": 19.278, "average": 20.227, "min_value": 17.86731358, "max_value": 22.58668642, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "2a4b34d6b4aa293887a44408bccd3f76", "metric_id": "0ad34641e41e43caf62be41d5f2eeda2", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_percent", "anomaly_score": -1.206516246, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 19.278, "min_metric_value": 17.86731358, "max_metric_value": 22.58668642, "training_avg": 20.227, "training_stddev": 0.7865621399, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_percent value is 19.278. The average for this metric is 20.227.", "is_anomalous": false}, {"value": 19.556, "average": 20.175384615, "min_value": 17.848194183, "max_value": 22.502575048, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "582895d718c7c6ee993c9bc4ec1ac400", "metric_id": "bf8f016adacba90c14708bc2a3ddac93", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_percent", "anomaly_score": -0.798453715, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 19.556, "min_metric_value": 17.848194183, "max_metric_value": 22.502575048, "training_avg": 20.175384615, "training_stddev": 0.7757301441, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_percent value is 19.556. The average for this metric is 20.175.", "is_anomalous": false}, {"value": 19.389, "average": 20.119214286, "min_value": 17.796122062, "max_value": 22.442306509, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "d5570dc4cec8498c54c03875441e0250", "metric_id": "9ac242a1ac86e7d1b45e162a181edab7", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_percent", "anomaly_score": -0.9429857477, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 19.389, "min_metric_value": 17.796122062, "max_metric_value": 22.442306509, "training_avg": 20.119214286, "training_stddev": 0.7743640744, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_percent value is 19.389. The average for this metric is 20.119.", "is_anomalous": false}, {"value": 19.111, "average": 20.052, "min_value": 17.681098815, "max_value": 22.422901185, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "125c094a82ab1979f6911b5ff2b1cbfd", "metric_id": "87fc270dbf5a2eed916987c7df1dbe56", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_percent", "anomaly_score": -1.190686486, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 19.111, "min_metric_value": 17.681098815, "max_metric_value": 22.422901185, "training_avg": 20.052, "training_stddev": 0.790300395, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_percent value is 19.111. The average for this metric is 20.052.", "is_anomalous": false}, {"value": 19.278, "average": 20.003625, "min_value": 17.640701419, "max_value": 22.366548581, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "665a880b2da5fb169bf5920168681ebb", "metric_id": "c322d5daa4fa037b4a75868496b26afd", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_percent", "anomaly_score": -0.9212633948, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 19.278, "min_metric_value": 17.640701419, "max_metric_value": 22.366548581, "training_avg": 20.003625, "training_stddev": 0.7876411937, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_percent value is 19.278. The average for this metric is 20.004.", "is_anomalous": false}, {"value": 19.889, "average": 19.996882353, "min_value": 17.707471786, "max_value": 22.28629292, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "f3ad8bd19d565fcd295802f13321b090", "metric_id": "6282a3c38bcc92f001c5df6f52e7b5a5", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_percent", "anomaly_score": -0.1413669805, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 19.889, "min_metric_value": 17.707471786, "max_metric_value": 22.28629292, "training_avg": 19.996882353, "training_stddev": 0.7631368555, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_percent value is 19.889. The average for this metric is 19.997.", "is_anomalous": false}, {"value": 19.167, "average": 19.950777778, "min_value": 17.653510834, "max_value": 22.248044722, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "36d553be26a80e8c1a159bde0693635f", "metric_id": "effcb80a6ee1390b160c76b6dfd40823", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_percent", "anomaly_score": -1.023535092, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 19.167, "min_metric_value": 17.653510834, "max_metric_value": 22.248044722, "training_avg": 19.950777778, "training_stddev": 0.765755648, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_percent value is 19.167. The average for this metric is 19.951.", "is_anomalous": false}, {"value": 20.278, "average": 19.968, "min_value": 17.724127455, "max_value": 22.211872545, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "8c997a3c9ed4a1106db4e62bb33a40a7", "metric_id": "56e4b511e757e97c2c6abca08f990874", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_percent", "anomaly_score": 0.4144620433, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 20.278, "min_metric_value": 17.724127455, "max_metric_value": 22.211872545, "training_avg": 19.968, "training_stddev": 0.7479575151, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_percent value is 20.278. The average for this metric is 19.968.", "is_anomalous": false}, {"value": 18.278, "average": 19.8835, "min_value": 17.422766188, "max_value": 22.344233812, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "af1874ded749a84be2890782cc2bfeea", "metric_id": "237f7488d75f3b45d507a7c8f708bdab", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_percent", "anomaly_score": -1.957342958, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 18.278, "min_metric_value": 17.422766188, "max_metric_value": 22.344233812, "training_avg": 19.8835, "training_stddev": 0.8202446041, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_percent value is 18.278. The average for this metric is 19.884.", "is_anomalous": false}, {"value": 20.778, "average": 19.926095238, "min_value": 17.457216279, "max_value": 22.394974197, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "542ae1fc83c6dd359577f042ac1ee58d", "metric_id": "a1652568afe02ad789ef181725020b5b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_percent", "anomaly_score": 1.035171966, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 20.778, "min_metric_value": 17.457216279, "max_metric_value": 22.394974197, "training_avg": 19.926095238, "training_stddev": 0.822959653, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_percent value is 20.778. The average for this metric is 19.926.", "is_anomalous": false}, {"value": 19.278, "average": 19.896636364, "min_value": 17.451858802, "max_value": 22.341413925, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "5309b589f785e6844dc43359ec0844f2", "metric_id": "574778e46a5241566264c8d86fffbee8", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_percent", "anomaly_score": -0.7591320864, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 19.278, "min_metric_value": 17.451858802, "max_metric_value": 22.341413925, "training_avg": 19.896636364, "training_stddev": 0.8149258538, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_percent value is 19.278. The average for this metric is 19.897.", "is_anomalous": false}, {"value": 19.111, "average": 19.862478261, "min_value": 17.423876001, "max_value": 22.301080521, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "22ac0abc69f3fad0798b45342d6f3817", "metric_id": "9de9da97d87217f91f8a8bc23bb3147d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_percent", "anomaly_score": -0.9244782633, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 19.111, "min_metric_value": 17.423876001, "max_metric_value": 22.301080521, "training_avg": 19.862478261, "training_stddev": 0.8128674201, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_percent value is 19.111. The average for this metric is 19.862.", "is_anomalous": false}, {"value": 19.111, "average": 19.831166667, "min_value": 17.402176109, "max_value": 22.260157225, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "5f4e01af6e5f798bfdeb2e85e99579ac", "metric_id": "1daa14e854070fbd52241b154c51edab", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_percent", "anomaly_score": -0.889464141, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 19.111, "min_metric_value": 17.402176109, "max_metric_value": 22.260157225, "training_avg": 19.831166667, "training_stddev": 0.8096635193, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_percent value is 19.111. The average for this metric is 19.831.", "is_anomalous": false}, {"value": 20.222, "average": 19.8468, "min_value": 17.457416764, "max_value": 22.236183236, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "a164f841d93c083c83f1667aec8e7c93", "metric_id": "578d36acb6fd195606e38c2bab0393a6", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_percent", "anomaly_score": 0.4710839111, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 20.222, "min_metric_value": 17.457416764, "max_metric_value": 22.236183236, "training_avg": 19.8468, "training_stddev": 0.7964610788, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_percent value is 20.222. The average for this metric is 19.847.", "is_anomalous": false}, {"value": 19.444, "average": 19.831307692, "min_value": 17.478235472, "max_value": 22.184379913, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "55fb4c5c0a93c0e6b5d7871aceb7819f", "metric_id": "e5273b2a52582d5e9013bc95cd771abd", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_percent", "anomaly_score": -0.4937898068, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 19.444, "min_metric_value": 17.478235472, "max_metric_value": 22.184379913, "training_avg": 19.831307692, "training_stddev": 0.7843574068, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_percent value is 19.444. The average for this metric is 19.831.", "is_anomalous": false}, {"value": 20.778, "average": 19.86637037, "min_value": 17.495140434, "max_value": 22.237600307, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "3ebfc6afd8ce26da54ee2042e1e40a9a", "metric_id": "ce510505ed796685c260c68403140af5", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_percent", "anomaly_score": 1.153363007, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 20.778, "min_metric_value": 17.495140434, "max_metric_value": 22.237600307, "training_avg": 19.86637037, "training_stddev": 0.7904099787, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_percent value is 20.778. The average for this metric is 19.866.", "is_anomalous": false}, {"value": 20.056, "average": 19.873142857, "min_value": 17.54375657, "max_value": 22.202529145, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "139b76cad57adf18c0e44f43294c6bc8", "metric_id": "885d2f7d48918bd8d249411e41b0484d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_percent", "anomaly_score": 0.2355004112, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 20.056, "min_metric_value": 17.54375657, "max_metric_value": 22.202529145, "training_avg": 19.873142857, "training_stddev": 0.7764620958, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_percent value is 20.056. The average for this metric is 19.873.", "is_anomalous": false}, {"value": 18.333, "average": 19.820034483, "min_value": 17.377002828, "max_value": 22.263066138, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "6c61121b224d88ee8155be1e962d46ac", "metric_id": "135b2e3d624e3924048e05fd851c2f8f", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_percent", "anomaly_score": -1.826052249, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 18.333, "min_metric_value": 17.377002828, "max_metric_value": 22.263066138, "training_avg": 19.820034483, "training_stddev": 0.814343885, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_percent value is 18.333. The average for this metric is 19.82.", "is_anomalous": false}, {"value": 68.0, "average": 21.426033333, "min_value": 17.377002828, "max_value": 22.263066138, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "350c6d593d97aa56b8b1e7197365dc90", "metric_id": "80d874f5cb2df1cd325cece913854773", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "zero_percent", "anomaly_score": 5.272879996, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 68.0, "min_metric_value": -5.072180188, "max_metric_value": 47.924246855, "training_avg": 21.426033333, "training_stddev": 8.83273784, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last zero_percent value is 68. The average for this metric is 21.426.", "is_anomalous": true}], "result_description": "In column NULL_PERCENT_INT, the last zero_percent value is 68. The average for this metric is 21.426."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_FLOAT, the last null_count value is 187. The average for this metric is 349.533.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Null Count", "metrics": [{"value": 386.0, "average": 389.5, "min_value": 374.650757595, "max_value": 404.349242405, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "55dde8a56bb8abc849cb2b369ef83d06", "metric_id": "2d005b9b79bf45fb17bd0b8dfdfd8d37", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_count", "anomaly_score": -0.7071067812, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 386.0, "min_metric_value": 374.650757595, "max_metric_value": 404.349242405, "training_avg": 389.5, "training_stddev": 4.949747468, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_count value is 386. The average for this metric is 389.5.", "is_anomalous": false}, {"value": 347.0, "average": 375.333333333, "min_value": 300.976087553, "max_value": 449.690579113, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "f5e97efb8aeac3a6dba3689dc1bb1b65", "metric_id": "d66e6376cdba44919b7a16251b5b6eb8", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_count", "anomaly_score": -1.143130022, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 347.0, "min_metric_value": 300.976087553, "max_metric_value": 449.690579113, "training_avg": 375.333333333, "training_stddev": 24.785748593, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_count value is 347. The average for this metric is 375.333.", "is_anomalous": false}, {"value": 363.0, "average": 372.25, "min_value": 308.781503878, "max_value": 435.718496122, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "257ac2721f09bb38005d6dc817c5b384", "metric_id": "a1339fe9c58fc61cb2f5c15a7f13360a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_count", "anomaly_score": -0.4372247918, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 363.0, "min_metric_value": 308.781503878, "max_metric_value": 435.718496122, "training_avg": 372.25, "training_stddev": 21.156165374, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_count value is 363. The average for this metric is 372.25.", "is_anomalous": false}, {"value": 370.0, "average": 371.8, "min_value": 316.751839268, "max_value": 426.848160732, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "56a6d6748b37621d4448569e0ba01f60", "metric_id": "a9ab845c29042ee83f3c09abcc99f7ef", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_count", "anomaly_score": -0.09809592052, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 370.0, "min_metric_value": 316.751839268, "max_metric_value": 426.848160732, "training_avg": 371.8, "training_stddev": 18.349386911, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_count value is 370. The average for this metric is 371.8.", "is_anomalous": false}, {"value": 347.0, "average": 367.666666667, "min_value": 309.815132547, "max_value": 425.518200786, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "638c3116f98e2976552739a8d3c3ebe9", "metric_id": "e21697e2b1abb4b49162c077a7d2fca1", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_count", "anomaly_score": -1.071708831, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 347.0, "min_metric_value": 309.815132547, "max_metric_value": 425.518200786, "training_avg": 367.666666667, "training_stddev": 19.283844707, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_count value is 347. The average for this metric is 367.667.", "is_anomalous": false}, {"value": 367.0, "average": 367.571428571, "min_value": 314.755035013, "max_value": 420.38782213, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "111254a01fff6daab04bcb69112be7da", "metric_id": "1508a2fe16ea2bd1a14dc7518e2d8292", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_count", "anomaly_score": -0.03245745494, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 367.0, "min_metric_value": 314.755035013, "max_metric_value": 420.38782213, "training_avg": 367.571428571, "training_stddev": 17.60546452, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_count value is 367. The average for this metric is 367.571.", "is_anomalous": false}, {"value": 343.0, "average": 364.5, "min_value": 309.089841055, "max_value": 419.910158945, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "fd84fbe3cde61b24e9c687ea7cf2a1ee", "metric_id": "1830b9627ddcb78512ff835f34766eaf", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_count", "anomaly_score": -1.164046471, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 343.0, "min_metric_value": 309.089841055, "max_metric_value": 419.910158945, "training_avg": 364.5, "training_stddev": 18.470052982, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_count value is 343. The average for this metric is 364.5.", "is_anomalous": false}, {"value": 380.0, "average": 366.222222222, "min_value": 312.122776754, "max_value": 420.321667691, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "1f47985a2dea8c1fdaf109e0a6c2e5cf", "metric_id": "0ba78ce3c189bf44c676a06bb0e460a6", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_count", "anomaly_score": 0.7640250834, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 380.0, "min_metric_value": 312.122776754, "max_metric_value": 420.321667691, "training_avg": 366.222222222, "training_stddev": 18.03314849, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_count value is 380. The average for this metric is 366.222.", "is_anomalous": false}, {"value": 393.0, "average": 368.9, "min_value": 311.91842403, "max_value": 425.88157597, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "851a0d994d0739c27089a5a0f1d756bd", "metric_id": "ca8df249222d320d5b2dccccfe298e07", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_count", "anomaly_score": 1.268831175, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 393.0, "min_metric_value": 311.91842403, "max_metric_value": 425.88157597, "training_avg": 368.9, "training_stddev": 18.993858657, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_count value is 393. The average for this metric is 368.9.", "is_anomalous": false}, {"value": 378.0, "average": 369.727272727, "min_value": 315.046712778, "max_value": 424.407832677, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "1c02bca80afd409830b95765a67e9c6c", "metric_id": "24430d8b13d6f359c4b8fdd49bc289c5", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_count", "anomaly_score": 0.4538757804, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 378.0, "min_metric_value": 315.046712778, "max_metric_value": 424.407832677, "training_avg": 369.727272727, "training_stddev": 18.226853316, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_count value is 378. The average for this metric is 369.727.", "is_anomalous": false}, {"value": 380.0, "average": 370.583333333, "min_value": 317.693868839, "max_value": 423.472797828, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "01bfc394ba40337ace0dc730f16e832e", "metric_id": "ce0ea45c688cbc36616700a1d5042b71", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_count", "anomaly_score": 0.5341328423, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 380.0, "min_metric_value": 317.693868839, "max_metric_value": 423.472797828, "training_avg": 370.583333333, "training_stddev": 17.629821498, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_count value is 380. The average for this metric is 370.583.", "is_anomalous": false}, {"value": 362.0, "average": 369.923076923, "min_value": 318.78412594, "max_value": 421.062027907, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "c6075965bb676bbed6a3eab6c62bbf5f", "metric_id": "28051113abde3873ebe35083038f758a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_count", "anomaly_score": -0.4647969955, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 362.0, "min_metric_value": 318.78412594, "max_metric_value": 421.062027907, "training_avg": 369.923076923, "training_stddev": 17.046316994, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_count value is 362. The average for this metric is 369.923.", "is_anomalous": false}, {"value": 364.0, "average": 369.5, "min_value": 320.138305103, "max_value": 418.861694897, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "5193fff680b571e08ab2fd306936d358", "metric_id": "0691e3275455de6754821d93322fa185", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_count", "anomaly_score": -0.3342672903, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 364.0, "min_metric_value": 320.138305103, "max_metric_value": 418.861694897, "training_avg": 369.5, "training_stddev": 16.453898299, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_count value is 364. The average for this metric is 369.5.", "is_anomalous": false}, {"value": 383.0, "average": 370.4, "min_value": 321.697990655, "max_value": 419.102009345, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "7ee9ea59968c3d400cfa592e91e705be", "metric_id": "d77d95f32b0e06eb8b28e0ca3ea11a13", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_count", "anomaly_score": 0.7761486745, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 383.0, "min_metric_value": 321.697990655, "max_metric_value": 419.102009345, "training_avg": 370.4, "training_stddev": 16.234003115, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_count value is 383. The average for this metric is 370.4.", "is_anomalous": false}, {"value": 353.0, "average": 369.3125, "min_value": 320.485637516, "max_value": 418.139362484, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "40f42bd4e06752d4f05e14f255d8162a", "metric_id": "436b5d381915fbe0b6b78e8a97d3f28a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_count", "anomaly_score": -1.002265915, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 353.0, "min_metric_value": 320.485637516, "max_metric_value": 418.139362484, "training_avg": 369.3125, "training_stddev": 16.275620828, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_count value is 353. The average for this metric is 369.313.", "is_anomalous": false}, {"value": 341.0, "average": 367.647058824, "min_value": 316.077360128, "max_value": 419.216757519, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "c22e878eab0aa756631415a01dc05935", "metric_id": "79b1457b309c148f11f44184590f5988", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_count", "anomaly_score": -1.550157912, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 341.0, "min_metric_value": 316.077360128, "max_metric_value": 419.216757519, "training_avg": 367.647058824, "training_stddev": 17.189899565, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_count value is 341. The average for this metric is 367.647.", "is_anomalous": false}, {"value": 368.0, "average": 367.666666667, "min_value": 317.636087782, "max_value": 417.697245551, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "dc6398bc9f84ff0bdff81bb2792cd1d7", "metric_id": "88651b23e4f4274015717fba090e6e29", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_count", "anomaly_score": 0.01998777592, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 368.0, "min_metric_value": 317.636087782, "max_metric_value": 417.697245551, "training_avg": 367.666666667, "training_stddev": 16.676859628, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_count value is 368. The average for this metric is 367.667.", "is_anomalous": false}, {"value": 346.0, "average": 366.526315789, "min_value": 315.669964947, "max_value": 417.382666632, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "e5582b46d555844af47b8836df45eb09", "metric_id": "9943cb14848669c735e573df3d085521", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_count", "anomaly_score": -1.210840856, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 346.0, "min_metric_value": 315.669964947, "max_metric_value": 417.382666632, "training_avg": 366.526315789, "training_stddev": 16.952116948, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_count value is 346. The average for this metric is 366.526.", "is_anomalous": false}, {"value": 330.0, "average": 364.7, "min_value": 309.467572454, "max_value": 419.932427546, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "1f60a9c379bda99123d11309d643e6af", "metric_id": "f64edd64b71a1d167f7b65deab72c496", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_count", "anomaly_score": -1.884762351, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 330.0, "min_metric_value": 309.467572454, "max_metric_value": 419.932427546, "training_avg": 364.7, "training_stddev": 18.410809182, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_count value is 330. The average for this metric is 364.7.", "is_anomalous": false}, {"value": 361.0, "average": 364.523809524, "min_value": 310.635432828, "max_value": 418.412186219, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "4182bb50a644dd5fbaf1472645c31bd6", "metric_id": "a5c869dd846dc3ab1a62c29f1fccddab", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_count", "anomaly_score": -0.196172704, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 361.0, "min_metric_value": 310.635432828, "max_metric_value": 418.412186219, "training_avg": 364.523809524, "training_stddev": 17.962792232, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_count value is 361. The average for this metric is 364.524.", "is_anomalous": false}, {"value": 341.0, "average": 363.454545455, "min_value": 308.75489328, "max_value": 418.154197629, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "ee46455cf52fd66e454d072a4d39df59", "metric_id": "92e2ebbe116f56e86dd992044d838534", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_count", "anomaly_score": -1.231518551, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 341.0, "min_metric_value": 308.75489328, "max_metric_value": 418.154197629, "training_avg": 363.454545455, "training_stddev": 18.233217392, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_count value is 341. The average for this metric is 363.455.", "is_anomalous": false}, {"value": 348.0, "average": 362.782608696, "min_value": 308.473218334, "max_value": 417.091999057, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "1672969ad7da5b90df0729729aec0765", "metric_id": "94bd218446855c7ec20cabd3e230fd79", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_count", "anomaly_score": -0.8165774978, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 348.0, "min_metric_value": 308.473218334, "max_metric_value": 417.091999057, "training_avg": 362.782608696, "training_stddev": 18.10313012, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_count value is 348. The average for this metric is 362.783.", "is_anomalous": false}, {"value": 381.0, "average": 363.541666667, "min_value": 309.267153071, "max_value": 417.816180263, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "7fb555ec0af5da0d9b5b5d9bcb5d2660", "metric_id": "d066734ef50d2d81e8493e94d9350399", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_count", "anomaly_score": 0.9650017389, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 381.0, "min_metric_value": 309.267153071, "max_metric_value": 417.816180263, "training_avg": 363.541666667, "training_stddev": 18.091504532, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_count value is 381. The average for this metric is 363.542.", "is_anomalous": false}, {"value": 370.0, "average": 363.8, "min_value": 310.527117593, "max_value": 417.072882407, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "ed91ff8ce992fc8ae3b4a868d2cdb945", "metric_id": "cfbe4f714c1490d73cd58ea5ed589291", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_count", "anomaly_score": 0.3491457409, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 370.0, "min_metric_value": 310.527117593, "max_metric_value": 417.072882407, "training_avg": 363.8, "training_stddev": 17.757627469, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_count value is 370. The average for this metric is 363.8.", "is_anomalous": false}, {"value": 361.0, "average": 363.692307692, "min_value": 311.46976614, "max_value": 415.914849244, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "913e7a935a674b33572931f5f49a7f2c", "metric_id": "dc6b334f4344c063390a7c4ce3b3baab", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_count", "anomaly_score": -0.1546635387, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 361.0, "min_metric_value": 311.46976614, "max_metric_value": 415.914849244, "training_avg": 363.692307692, "training_stddev": 17.407513851, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_count value is 361. The average for this metric is 363.692.", "is_anomalous": false}, {"value": 376.0, "average": 364.148148148, "min_value": 312.449068158, "max_value": 415.847228138, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "e98e519b32a2c21cf604d65782283afe", "metric_id": "5dcb08b54f08e195082b8566a43d2ccf", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_count", "anomaly_score": 0.6877405858, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 376.0, "min_metric_value": 312.449068158, "max_metric_value": 415.847228138, "training_avg": 364.148148148, "training_stddev": 17.233026663, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_count value is 376. The average for this metric is 364.148.", "is_anomalous": false}, {"value": 391.0, "average": 365.107142857, "min_value": 312.139610124, "max_value": 418.07467559, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "53c0704d9a78f67d2fe4abd3aecafd17", "metric_id": "416bc4c658d5cb36afcecd870724126d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_count", "anomaly_score": 1.466531806, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 391.0, "min_metric_value": 312.139610124, "max_metric_value": 418.07467559, "training_avg": 365.107142857, "training_stddev": 17.655844244, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_count value is 391. The average for this metric is 365.107.", "is_anomalous": false}, {"value": 76.0, "average": 355.137931034, "min_value": 312.139610124, "max_value": 418.07467559, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "9ff6ce0372bf0cda8f9c42b21a338f47", "metric_id": "409807a991f14e1f5ab0b872128fd504", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_count", "anomaly_score": -4.947849709, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 76.0, "min_metric_value": 185.889905673, "max_metric_value": 524.385956396, "training_avg": 355.137931034, "training_stddev": 56.416008454, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_count value is 76. The average for this metric is 355.138.", "is_anomalous": true}, {"value": 187.0, "average": 349.533333333, "min_value": 159.432706622, "max_value": 539.633960044, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "4b5c7cb6da96f353e685ede0af09bc6c", "metric_id": "457fce6f1499f1fa710016d2da33c777", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_count", "anomaly_score": -2.564957351, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 187.0, "min_metric_value": 159.432706622, "max_metric_value": 539.633960044, "training_avg": 349.533333333, "training_stddev": 63.36687557, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_count value is 187. The average for this metric is 349.533.", "is_anomalous": false}], "result_description": "In column NULL_PERCENT_FLOAT, the last null_count value is 187. The average for this metric is 349.533."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_FLOAT, the last null_percent value is 62.333. The average for this metric is 21.854.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Null Percent", "metrics": [{"value": 21.444, "average": 21.6385, "min_value": 20.813306386, "max_value": 22.463693614, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "15a5ea61fad3593e23dd6c7289fea802", "metric_id": "e4a66fc58a47beb26366d19b68ddc23f", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_percent", "anomaly_score": -0.7071067812, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 21.444, "min_metric_value": 20.813306386, "max_metric_value": 22.463693614, "training_avg": 21.6385, "training_stddev": 0.2750645379, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_percent value is 21.444. The average for this metric is 21.639.", "is_anomalous": false}, {"value": 19.278, "average": 20.851666667, "min_value": 16.72173289, "max_value": 24.981600443, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "1605c0310fda0d7119d4ed2b80f49bdc", "metric_id": "68ba1a14afc060d41e9e69142c3986a4", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_percent", "anomaly_score": -1.143117603, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 19.278, "min_metric_value": 16.72173289, "max_metric_value": 24.981600443, "training_avg": 20.851666667, "training_stddev": 1.376644592, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_percent value is 19.278. The average for this metric is 20.852.", "is_anomalous": false}, {"value": 20.167, "average": 20.6805, "min_value": 17.155499149, "max_value": 24.205500851, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "a4930aa5dea56371d595c8e8663c4f38", "metric_id": "3770f7d2e4933439612763027c87e99c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_percent", "anomaly_score": -0.4370211711, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 20.167, "min_metric_value": 17.155499149, "max_metric_value": 24.205500851, "training_avg": 20.6805, "training_stddev": 1.175000284, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_percent value is 20.167. The average for this metric is 20.681.", "is_anomalous": false}, {"value": 20.556, "average": 20.6556, "min_value": 17.598293391, "max_value": 23.712906609, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "44a2242f35d34305add550565e4d4634", "metric_id": "680e907bb8b028bb674018174bfa4b3f", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_percent", "anomaly_score": -0.09773308282, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 20.556, "min_metric_value": 17.598293391, "max_metric_value": 23.712906609, "training_avg": 20.6556, "training_stddev": 1.019102203, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_percent value is 20.556. The average for this metric is 20.656.", "is_anomalous": false}, {"value": 19.278, "average": 20.426, "min_value": 17.212843981, "max_value": 23.639156019, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "d2d9a8986de6e17eca4b7184f806fcad", "metric_id": "72cb95483035290b55b37d59e0aafc88", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_percent", "anomaly_score": -1.071843378, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 19.278, "min_metric_value": 17.212843981, "max_metric_value": 23.639156019, "training_avg": 20.426, "training_stddev": 1.071052006, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_percent value is 19.278. The average for this metric is 20.426.", "is_anomalous": false}, {"value": 20.389, "average": 20.420714286, "min_value": 17.487217542, "max_value": 23.354211029, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "daa613c9608d24b4ad7643a9a83a6bc5", "metric_id": "a462eb4f3523f1689a5939d12b7dc224", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_percent", "anomaly_score": -0.03243325815, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 20.389, "min_metric_value": 17.487217542, "max_metric_value": 23.354211029, "training_avg": 20.420714286, "training_stddev": 0.9778322478, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_percent value is 20.389. The average for this metric is 20.421.", "is_anomalous": false}, {"value": 19.056, "average": 20.250125, "min_value": 17.17257556, "max_value": 23.32767444, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "8e650f0140b975ea2074ed8c0ccb8fd5", "metric_id": "b1add912f2b65c359bd3fe2b542865bc", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_percent", "anomaly_score": -1.164034915, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 19.056, "min_metric_value": 17.17257556, "max_metric_value": 23.32767444, "training_avg": 20.250125, "training_stddev": 1.025849813, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_percent value is 19.056. The average for this metric is 20.25.", "is_anomalous": false}, {"value": 21.111, "average": 20.345777778, "min_value": 17.341031116, "max_value": 23.350524439, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "cd2c24d441d4ea8004fd9e87fece72c5", "metric_id": "af3acf5930665437b26c09a8b7c256cc", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_percent", "anomaly_score": 0.7640133846, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 21.111, "min_metric_value": 17.341031116, "max_metric_value": 23.350524439, "training_avg": 20.345777778, "training_stddev": 1.001582221, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_percent value is 21.111. The average for this metric is 20.346.", "is_anomalous": false}, {"value": 21.833, "average": 20.4945, "min_value": 17.329696294, "max_value": 23.659303706, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "71d449aa42f86970a6c0b929919d27fa", "metric_id": "c2e900b521c06ef38620a2b68e29e4df", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_percent", "anomaly_score": 1.26879907, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 21.833, "min_metric_value": 17.329696294, "max_metric_value": 23.659303706, "training_avg": 20.4945, "training_stddev": 1.054934569, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_percent value is 21.833. The average for this metric is 20.495.", "is_anomalous": false}, {"value": 21.0, "average": 20.540454545, "min_value": 17.503440477, "max_value": 23.577468614, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "58929fa47618b6397e058b01fc011f87", "metric_id": "d2340eb04f1b08f55a6594c43e02b0bb", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_percent", "anomaly_score": 0.4539446747, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 21.0, "min_metric_value": 17.503440477, "max_metric_value": 23.577468614, "training_avg": 20.540454545, "training_stddev": 1.012338023, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_percent value is 21. The average for this metric is 20.54.", "is_anomalous": false}, {"value": 21.111, "average": 20.588, "min_value": 17.650466991, "max_value": 23.525533009, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "86363adfd4016425071ccf99679bfe22", "metric_id": "b4c955fbc1e9d1e60c825131d7f1051f", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_percent", "anomaly_score": 0.5341216575, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 21.111, "min_metric_value": 17.650466991, "max_metric_value": 23.525533009, "training_avg": 20.588, "training_stddev": 0.9791776698, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_percent value is 21.111. The average for this metric is 20.588.", "is_anomalous": false}, {"value": 20.111, "average": 20.551307692, "min_value": 17.710968174, "max_value": 23.39164721, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "e28f7ef728c790f52a9213be5152c292", "metric_id": "bbabd0c9ca74055a1f64bff996babf44", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_percent", "anomaly_score": -0.4650581624, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 20.111, "min_metric_value": 17.710968174, "max_metric_value": 23.39164721, "training_avg": 20.551307692, "training_stddev": 0.9467798393, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_percent value is 20.111. The average for this metric is 20.551.", "is_anomalous": false}, {"value": 20.222, "average": 20.527785714, "min_value": 17.78613235, "max_value": 23.269439079, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "5a5517613933f7fd5af7ab63a3f81066", "metric_id": "55722a67f87e4c52024c0d64198bb847", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_percent", "anomaly_score": -0.3345999734, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 20.222, "min_metric_value": 17.78613235, "max_metric_value": 23.269439079, "training_avg": 20.527785714, "training_stddev": 0.9138844548, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_percent value is 20.222. The average for this metric is 20.528.", "is_anomalous": false}, {"value": 21.278, "average": 20.5778, "min_value": 17.872721158, "max_value": 23.282878842, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "df525291fafd35f99e407d4fd149f128", "metric_id": "edf15e5193d16d1ef28ab1516edd84cf", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_percent", "anomaly_score": 0.7765392887, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 21.278, "min_metric_value": 17.872721158, "max_metric_value": 23.282878842, "training_avg": 20.5778, "training_stddev": 0.9016929474, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_percent value is 21.278. The average for this metric is 20.578.", "is_anomalous": false}, {"value": 19.611, "average": 20.517375, "min_value": 17.805292507, "max_value": 23.229457493, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "335b2fb994393db797c2a381d726c789", "metric_id": "e9bcf2b363bdff33ed2f33a9b4dfef8b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_percent", "anomaly_score": -1.002596716, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 19.611, "min_metric_value": 17.805292507, "max_metric_value": 23.229457493, "training_avg": 20.517375, "training_stddev": 0.9040274977, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_percent value is 19.611. The average for this metric is 20.517.", "is_anomalous": false}, {"value": 18.944, "average": 20.424823529, "min_value": 17.560169447, "max_value": 23.289477612, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "f00e171440d0a59d02162d57ee03f995", "metric_id": "abf707a13646adc713b873dfe2c5bf99", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_percent", "anomaly_score": -1.550787795, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 18.944, "min_metric_value": 17.560169447, "max_metric_value": 23.289477612, "training_avg": 20.424823529, "training_stddev": 0.9548846943, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_percent value is 18.944. The average for this metric is 20.425.", "is_anomalous": false}, {"value": 20.444, "average": 20.425888889, "min_value": 17.646733135, "max_value": 23.205044643, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "e080ea1129138418c1600bc427d8391c", "metric_id": "b8b71a4cac82979530633923f63ef8bb", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_percent", "anomaly_score": 0.01955030165, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 20.444, "min_metric_value": 17.646733135, "max_metric_value": 23.205044643, "training_avg": 20.425888889, "training_stddev": 0.9263852514, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_percent value is 20.444. The average for this metric is 20.426.", "is_anomalous": false}, {"value": 19.222, "average": 20.362526316, "min_value": 17.537434217, "max_value": 23.187618415, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "5d0843cb2ef97c18265d2228e8b09317", "metric_id": "c8c720e3f4afa68be7e2e62072d97964", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_percent", "anomaly_score": -1.211138904, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 19.222, "min_metric_value": 17.537434217, "max_metric_value": 23.187618415, "training_avg": 20.362526316, "training_stddev": 0.9416973664, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_percent value is 19.222. The average for this metric is 20.363.", "is_anomalous": false}, {"value": 18.333, "average": 20.26105, "min_value": 17.192723947, "max_value": 23.329376053, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "0f55be825ff123c96f331005a98c148e", "metric_id": "da0adbc15a55f85e8fac1885167e34f2", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_percent", "anomaly_score": -1.885115826, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 18.333, "min_metric_value": 17.192723947, "max_metric_value": 23.329376053, "training_avg": 20.26105, "training_stddev": 1.022775351, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_percent value is 18.333. The average for this metric is 20.261.", "is_anomalous": false}, {"value": 20.056, "average": 20.251285714, "min_value": 17.257640273, "max_value": 23.244931156, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "fa58fd24eefc313f7f5103f798c0a974", "metric_id": "b3359823bdee7aadfe3a938effc32b2b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_percent", "anomaly_score": -0.1957002438, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 20.056, "min_metric_value": 17.257640273, "max_metric_value": 23.244931156, "training_avg": 20.251285714, "training_stddev": 0.9978818138, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_percent value is 20.056. The average for this metric is 20.251.", "is_anomalous": false}, {"value": 18.944, "average": 20.191863636, "min_value": 17.153066182, "max_value": 23.230661091, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "8713707ed3efeec47a464cce25a8c055", "metric_id": "9a18af5b6de2502b592389981bfe46b7", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_percent", "anomaly_score": -1.231931698, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 18.944, "min_metric_value": 17.153066182, "max_metric_value": 23.230661091, "training_avg": 20.191863636, "training_stddev": 1.012932485, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_percent value is 18.944. The average for this metric is 20.192.", "is_anomalous": false}, {"value": 19.333, "average": 20.154521739, "min_value": 17.137371777, "max_value": 23.171671701, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "441ba253d149c1cfffe99966c467ab63", "metric_id": "aba55369787c84639222bd02a9304977", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_percent", "anomaly_score": -0.8168520784, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 19.333, "min_metric_value": 17.137371777, "max_metric_value": 23.171671701, "training_avg": 20.154521739, "training_stddev": 1.005716654, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_percent value is 19.333. The average for this metric is 20.155.", "is_anomalous": false}, {"value": 21.167, "average": 20.196708333, "min_value": 17.181443844, "max_value": 23.211972823, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "76e1de1472b0e1a51d177bdef6ec688e", "metric_id": "62a308879f2e7c61f946bbde4bc2a041", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_percent", "anomaly_score": 0.9653796575, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 21.167, "min_metric_value": 17.181443844, "max_metric_value": 23.211972823, "training_avg": 20.196708333, "training_stddev": 1.005088163, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_percent value is 21.167. The average for this metric is 20.197.", "is_anomalous": false}, {"value": 20.556, "average": 20.21108, "min_value": 17.251440378, "max_value": 23.170719622, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "56c454280c405c50294cd714f1cf82fe", "metric_id": "cfe31fe370320af9cddc34c92198e60d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_percent", "anomaly_score": 0.3496236476, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 20.556, "min_metric_value": 17.251440378, "max_metric_value": 23.170719622, "training_avg": 20.21108, "training_stddev": 0.9865465405, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_percent value is 20.556. The average for this metric is 20.211.", "is_anomalous": false}, {"value": 20.056, "average": 20.205115385, "min_value": 17.30383757, "max_value": 23.106393199, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "de8fe5db263b9bcd67eafaf5fc0249cf", "metric_id": "f6f53fcbb427a8208bda691527e7d4cd", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_percent", "anomaly_score": -0.1541893547, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 20.056, "min_metric_value": 17.30383757, "max_metric_value": 23.106393199, "training_avg": 20.205115385, "training_stddev": 0.9670926047, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_percent value is 20.056. The average for this metric is 20.205.", "is_anomalous": false}, {"value": 20.889, "average": 20.230444444, "min_value": 17.358238725, "max_value": 23.102650163, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "08bf1c207ca9cfe95ee6d9b14a3b4182", "metric_id": "e352c6f02cdecc4f896601a933abe4fb", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_percent", "anomaly_score": 0.6878569504, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 20.889, "min_metric_value": 17.358238725, "max_metric_value": 23.102650163, "training_avg": 20.230444444, "training_stddev": 0.9574019063, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_percent value is 20.889. The average for this metric is 20.23.", "is_anomalous": false}, {"value": 21.722, "average": 20.283714286, "min_value": 17.341075917, "max_value": 23.226352655, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "cd316358099c5f46c1194d029cf7f1e4", "metric_id": "1dba1bb8b587e420c256ca3a3da02c5e", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_percent", "anomaly_score": 1.4663226, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 21.722, "min_metric_value": 17.341075917, "max_metric_value": 23.226352655, "training_avg": 20.283714286, "training_stddev": 0.9808794564, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_percent value is 21.722. The average for this metric is 20.284.", "is_anomalous": false}, {"value": 25.333, "average": 20.457827586, "min_value": 17.341075917, "max_value": 23.226352655, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "b62f2e990478ca23a7b8f6a95a256ebd", "metric_id": "ee86f9c9a63ef6ba9a774402cee475e2", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_percent", "anomaly_score": 3.626783509, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 25.333, "min_metric_value": 16.42518621, "max_metric_value": 24.490468963, "training_avg": 20.457827586, "training_stddev": 1.344213792, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_percent value is 25.333. The average for this metric is 20.458.", "is_anomalous": true}, {"value": 62.333, "average": 21.853666667, "min_value": 16.42518621, "max_value": 24.490468963, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "1efe923e8bd1d92522ffe9e94505e00e", "metric_id": "aadbbd35461501813b27a38bf9e11c8d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "null_percent", "anomaly_score": 5.217361839, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 62.333, "min_metric_value": -1.422081452, "max_metric_value": 45.129414785, "training_avg": 21.853666667, "training_stddev": 7.758582706, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last null_percent value is 62.333. The average for this metric is 21.854.", "is_anomalous": true}], "result_description": "In column NULL_PERCENT_FLOAT, the last null_percent value is 62.333. The average for this metric is 21.854."}}, {"metadata": {"test_unique_id": "model.elementary_integration_tests.any_type_column_anomalies.generic_test_on_model_any_type_column_anomalies_.generic_test_on_model", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": null, "test_name": "generic_test_on_model", "test_display_name": "Generic Test On Model", "latest_run_time": "2023-01-02T12:46:31+02:00", "latest_run_time_utc": "2023-01-02T10:46:31+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "dbt_test", "test_sub_type": "generic", "test_query": "with nothing as (select 1 as num)\n select * from nothing where num = 1", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "Got 1 result, configured to fail if != 0", "result_query": "with nothing as (select 1 as num)\n select * from nothing where num = 1"}, "configuration": {"test_name": "generic_test_on_model", "test_params": null}}, "test_results": {"display_name": "generic_test_on_model", "results_sample": [{"num": 1.0}], "error_message": "Got 1 result, configured to fail if != 0", "failed_rows_count": 1}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "standard_deviation", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('standard_deviation' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_INT, the last standard_deviation value is 31.187. The average for this metric is 29.256.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('standard_deviation' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Standard Deviation", "metrics": [{"value": 29.118081777, "average": 29.137964148, "min_value": 29.053610393, "max_value": 29.222317902, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "54bc72684c63843125dceed33938346c", "metric_id": "0697b52aec4a638f7275b78c3aef3086", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "standard_deviation", "anomaly_score": -0.7071067811, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 29.118081777, "min_metric_value": 29.053610393, "max_metric_value": 29.222317902, "training_avg": 29.137964148, "training_stddev": 0.0281179181, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last standard_deviation value is 29.118. The average for this metric is 29.138.", "is_anomalous": false}, {"value": 29.085419168, "average": 29.120449155, "min_value": 29.011634162, "max_value": 29.229264147, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "951378ae8c4afc5ab165386bb6469d99", "metric_id": "67ef6a3f5f76bf53a9ae93eff357b30c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "standard_deviation", "anomaly_score": -0.965767279, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 29.085419168, "min_metric_value": 29.011634162, "max_metric_value": 29.229264147, "training_avg": 29.120449155, "training_stddev": 0.0362716641, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last standard_deviation value is 29.085. The average for this metric is 29.12.", "is_anomalous": false}, {"value": 29.09628097, "average": 29.114407109, "min_value": 29.018448632, "max_value": 29.210365585, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "336af897bc62f02e3163e3a215f707de", "metric_id": "fdb0082c22e8339c469383a5ce49c1df", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "standard_deviation", "anomaly_score": -0.5666869322, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 29.09628097, "min_metric_value": 29.018448632, "max_metric_value": 29.210365585, "training_avg": 29.114407109, "training_stddev": 0.0319861589, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last standard_deviation value is 29.096. The average for this metric is 29.114.", "is_anomalous": false}, {"value": 29.071840932, "average": 29.105893873, "min_value": 29.005060323, "max_value": 29.206727423, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "be1ba4e2ffd090eb616175373f97afea", "metric_id": "1f645a6337e7f822343a6d6583521ccf", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "standard_deviation", "anomaly_score": -1.013143175, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 29.071840932, "min_metric_value": 29.005060323, "max_metric_value": 29.206727423, "training_avg": 29.105893873, "training_stddev": 0.03361118328, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last standard_deviation value is 29.072. The average for this metric is 29.106.", "is_anomalous": false}, {"value": 29.106008923, "average": 29.105913048, "min_value": 29.015724669, "max_value": 29.196101427, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "67764e2f18e679c0c5e9b13c742bbf04", "metric_id": "ccf3a6a5a77fc33f1903d4189c2d5520", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "standard_deviation", "anomaly_score": 0.003189143612, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 29.106008923, "min_metric_value": 29.015724669, "max_metric_value": 29.196101427, "training_avg": 29.105913048, "training_stddev": 0.03006279294, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last standard_deviation value is 29.106. The average for this metric is 29.106.", "is_anomalous": false}, {"value": 29.60330685, "average": 29.176969306, "min_value": 28.607000215, "max_value": 29.746938396, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "29fe3bade6c092d509999b9eef08a003", "metric_id": "43bd778461fb0e430ebc1fbae0f5e6e4", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "standard_deviation", "anomaly_score": 2.244003497, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 29.60330685, "min_metric_value": 28.607000215, "max_metric_value": 29.746938396, "training_avg": 29.176969306, "training_stddev": 0.1899896969, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last standard_deviation value is 29.603. The average for this metric is 29.177.", "is_anomalous": false}, {"value": 29.214665115, "average": 29.181681282, "min_value": 28.652479895, "max_value": 29.710882669, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "ac8d48acdef144c978d51ef4ec100ef6", "metric_id": "aecb74399d92ba69d1a5659fd2d74544", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "standard_deviation", "anomaly_score": 0.1869826907, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 29.214665115, "min_metric_value": 28.652479895, "max_metric_value": 29.710882669, "training_avg": 29.181681282, "training_stddev": 0.1764004623, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last standard_deviation value is 29.215. The average for this metric is 29.182.", "is_anomalous": false}, {"value": 29.416688908, "average": 29.20779324, "min_value": 28.659818853, "max_value": 29.755767628, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "7fd35d9e43ef609f71ec3f0feb43662b", "metric_id": "b137351c1cbef6ccec30983ccc45728c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "standard_deviation", "anomaly_score": 1.143642876, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 29.416688908, "min_metric_value": 28.659818853, "max_metric_value": 29.755767628, "training_avg": 29.20779324, "training_stddev": 0.1826581291, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last standard_deviation value is 29.417. The average for this metric is 29.208.", "is_anomalous": false}, {"value": 29.143299965, "average": 29.201343913, "min_value": 28.681098412, "max_value": 29.721589414, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "480cf4055b24618a59655e9d054f2e0e", "metric_id": "0524370ba7295780a5d2bc8599314b9e", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "standard_deviation", "anomaly_score": -0.3347109079, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 29.143299965, "min_metric_value": 28.681098412, "max_metric_value": 29.721589414, "training_avg": 29.201343913, "training_stddev": 0.173415167, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last standard_deviation value is 29.143. The average for this metric is 29.201.", "is_anomalous": false}, {"value": 28.84949342, "average": 29.169357504, "min_value": 28.582092874, "max_value": 29.756622134, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "7a69152fb7260a4413cca183034e6665", "metric_id": "ba440e7bc7f3beab5fff225929c3dd57", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "standard_deviation", "anomaly_score": -1.634003147, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 28.84949342, "min_metric_value": 28.582092874, "max_metric_value": 29.756622134, "training_avg": 29.169357504, "training_stddev": 0.1957548766, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last standard_deviation value is 28.849. The average for this metric is 29.169.", "is_anomalous": false}, {"value": 29.696009878, "average": 29.213245202, "min_value": 28.49106165, "max_value": 29.935428755, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "67be24474f96f4fdaa9a4f94adb1a8bb", "metric_id": "d5a2fd15e32d72455ba9616c6d265402", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "standard_deviation", "anomaly_score": 2.00543757, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 29.696009878, "min_metric_value": 28.49106165, "max_metric_value": 29.935428755, "training_avg": 29.213245202, "training_stddev": 0.2407278508, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last standard_deviation value is 29.696. The average for this metric is 29.213.", "is_anomalous": false}, {"value": 29.286802098, "average": 29.218903425, "min_value": 28.524761889, "max_value": 29.913044961, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "40a2683fd60825ac278018930c444112", "metric_id": "6e978a335915d038aed9b664e8c7094a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "standard_deviation", "anomaly_score": 0.2934502665, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 29.286802098, "min_metric_value": 28.524761889, "max_metric_value": 29.913044961, "training_avg": 29.218903425, "training_stddev": 0.2313805119, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last standard_deviation value is 29.287. The average for this metric is 29.219.", "is_anomalous": false}, {"value": 28.871586005, "average": 29.194095038, "min_value": 28.471380778, "max_value": 29.916809297, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "6ce73bd9abfc553c05e65f5782b84039", "metric_id": "16141910c75681c7c4b0927fe1471a18", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "standard_deviation", "anomaly_score": -1.338740846, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 28.871586005, "min_metric_value": 28.471380778, "max_metric_value": 29.916809297, "training_avg": 29.194095038, "training_stddev": 0.2409047531, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last standard_deviation value is 28.872. The average for this metric is 29.194.", "is_anomalous": false}, {"value": 28.797722334, "average": 29.167670191, "min_value": 28.4065694, "max_value": 29.928770982, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "93d0e0dd059998d223213b20a0c463b7", "metric_id": "157385cd72999ce2efa0d1f861bbd0db", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "standard_deviation", "anomaly_score": -1.458208405, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 28.797722334, "min_metric_value": 28.4065694, "max_metric_value": 29.928770982, "training_avg": 29.167670191, "training_stddev": 0.2537002637, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last standard_deviation value is 28.798. The average for this metric is 29.168.", "is_anomalous": false}, {"value": 28.994698158, "average": 29.156859439, "min_value": 28.410209762, "max_value": 29.903509115, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "2e654dff226fc30ca1e2faa78b9d20f1", "metric_id": "ccd49b8c0c5185da0a701bd1374469c0", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "standard_deviation", "anomaly_score": -0.6515556867, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 28.994698158, "min_metric_value": 28.410209762, "max_metric_value": 29.903509115, "training_avg": 29.156859439, "training_stddev": 0.2488832256, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last standard_deviation value is 28.995. The average for this metric is 29.157.", "is_anomalous": false}, {"value": 28.344050378, "average": 29.109047141, "min_value": 28.17502182, "max_value": 30.043072462, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "f15d8b0abb7b851bc8d89fbac3ebe00f", "metric_id": "0e91b2883a93b9b2440b5b40efb66d7f", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "standard_deviation", "anomaly_score": -2.457096437, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 28.344050378, "min_metric_value": 28.17502182, "max_metric_value": 30.043072462, "training_avg": 29.109047141, "training_stddev": 0.3113417735, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last standard_deviation value is 28.344. The average for this metric is 29.109.", "is_anomalous": false}, {"value": 29.205929214, "average": 29.114429478, "min_value": 28.205705909, "max_value": 30.023153048, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "e7f89078c199f53a1942caffdb28439e", "metric_id": "eae40b6873691f4f80fc1f6a08c585a4", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "standard_deviation", "anomaly_score": 0.302071185, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 29.205929214, "min_metric_value": 28.205705909, "max_metric_value": 30.023153048, "training_avg": 29.114429478, "training_stddev": 0.3029078566, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last standard_deviation value is 29.206. The average for this metric is 29.114.", "is_anomalous": false}, {"value": 29.700447977, "average": 29.145272557, "min_value": 28.174410325, "max_value": 30.11613479, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "76ad3d93f9bd701238d3614960bc2783", "metric_id": "e3bc893d37cdc3f993df1e597b6f888a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "standard_deviation", "anomaly_score": 1.715512463, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 29.700447977, "min_metric_value": 28.174410325, "max_metric_value": 30.11613479, "training_avg": 29.145272557, "training_stddev": 0.3236207442, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last standard_deviation value is 29.7. The average for this metric is 29.145.", "is_anomalous": false}, {"value": 28.866795684, "average": 29.131348714, "min_value": 28.168093005, "max_value": 30.094604422, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "3436afebb00591e224cef81c3a4a455b", "metric_id": "80969b398de4cc98d6ce21ae43a51460", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "standard_deviation", "anomaly_score": -0.8239339586, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 28.866795684, "min_metric_value": 28.168093005, "max_metric_value": 30.094604422, "training_avg": 29.131348714, "training_stddev": 0.3210852363, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last standard_deviation value is 28.867. The average for this metric is 29.131.", "is_anomalous": false}, {"value": 29.304603981, "average": 29.139598964, "min_value": 28.193907113, "max_value": 30.085290816, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "63d1f88d82ab9e9bcf5cf54155247df3", "metric_id": "626c6360eb49d3da58a908674aec5626", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "standard_deviation", "anomaly_score": 0.5234422286, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 29.304603981, "min_metric_value": 28.193907113, "max_metric_value": 30.085290816, "training_avg": 29.139598964, "training_stddev": 0.315230617, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last standard_deviation value is 29.305. The average for this metric is 29.14.", "is_anomalous": false}, {"value": 29.387438526, "average": 29.150864399, "min_value": 28.214448908, "max_value": 30.08727989, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "c06d4cacf5c15600b52108e194fb265a", "metric_id": "b30a3f339af2f90c0e1cba70863bb9b4", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "standard_deviation", "anomaly_score": 0.7579139697, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 29.387438526, "min_metric_value": 28.214448908, "max_metric_value": 30.08727989, "training_avg": 29.150864399, "training_stddev": 0.3121384969, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last standard_deviation value is 29.387. The average for this metric is 29.151.", "is_anomalous": false}, {"value": 28.87036006, "average": 29.138668558, "min_value": 28.207108072, "max_value": 30.070229045, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "d7c7e7bddb2867d0d43665e4c1d6d76e", "metric_id": "9e43a69d0e0b680d16198bd094d68337", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "standard_deviation", "anomaly_score": -0.8640614384, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 28.87036006, "min_metric_value": 28.207108072, "max_metric_value": 30.070229045, "training_avg": 29.138668558, "training_stddev": 0.3105201621, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last standard_deviation value is 28.87. The average for this metric is 29.139.", "is_anomalous": false}, {"value": 28.985518435, "average": 29.132287303, "min_value": 28.216388891, "max_value": 30.048185715, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "f9c498fa542f1abfa8328e95103ec541", "metric_id": "fc70bf4a1bb22aeb22c3bae3be7ae4e3", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "standard_deviation", "anomaly_score": -0.4807373833, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 28.985518435, "min_metric_value": 28.216388891, "max_metric_value": 30.048185715, "training_avg": 29.132287303, "training_stddev": 0.3052994705, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last standard_deviation value is 28.986. The average for this metric is 29.132.", "is_anomalous": false}, {"value": 29.5727785, "average": 29.149906951, "min_value": 28.215151001, "max_value": 30.084662901, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "45f498df64034be7b49b71a442a0236d", "metric_id": "2f983b1f38e15921b7e2d657f65b229d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "standard_deviation", "anomaly_score": 1.35716135, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 29.5727785, "min_metric_value": 28.215151001, "max_metric_value": 30.084662901, "training_avg": 29.149906951, "training_stddev": 0.3115853166, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last standard_deviation value is 29.573. The average for this metric is 29.15.", "is_anomalous": false}, {"value": 29.305678304, "average": 29.155898157, "min_value": 28.2354541, "max_value": 30.076342214, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "51f0a4bd9808a99e614fb2e47fa2b920", "metric_id": "74381bda3efe4a899d69baf224408c2b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "standard_deviation", "anomaly_score": 0.4881778933, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 29.305678304, "min_metric_value": 28.2354541, "max_metric_value": 30.076342214, "training_avg": 29.155898157, "training_stddev": 0.3068146858, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last standard_deviation value is 29.306. The average for this metric is 29.156.", "is_anomalous": false}, {"value": 29.175158506, "average": 29.156611503, "min_value": 28.253973349, "max_value": 30.059249657, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "f9e343a7270ec4b3f813a74abeb7994e", "metric_id": "a7f050cee745de12fec0c2a1d09876fe", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "standard_deviation", "anomaly_score": 0.06164265178, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 29.175158506, "min_metric_value": 28.253973349, "max_metric_value": 30.059249657, "training_avg": 29.156611503, "training_stddev": 0.3008793847, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last standard_deviation value is 29.175. The average for this metric is 29.157.", "is_anomalous": false}, {"value": 29.003266334, "average": 29.15113489, "min_value": 28.261113648, "max_value": 30.041156132, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "c9f24a855cfbcdf9838a81b17b33d176", "metric_id": "8eb589d7ac97ede7227c7763befbac4f", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "standard_deviation", "anomaly_score": -0.4984214398, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 29.003266334, "min_metric_value": 28.261113648, "max_metric_value": 30.041156132, "training_avg": 29.15113489, "training_stddev": 0.2966737475, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last standard_deviation value is 29.003. The average for this metric is 29.151.", "is_anomalous": false}, {"value": 30.261552495, "average": 29.189425152, "min_value": 28.261113648, "max_value": 30.041156132, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "36bc16e932bbf19d14d93ec1fd9a47bc", "metric_id": "f4c9184192aab6714a2b79d989643bb2", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "standard_deviation", "anomaly_score": 3.003852134, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 30.261552495, "min_metric_value": 28.118672703, "max_metric_value": 30.260177601, "training_avg": 29.189425152, "training_stddev": 0.3569174831, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last standard_deviation value is 30.262. The average for this metric is 29.189.", "is_anomalous": true}, {"value": 31.186897872, "average": 29.256007576, "min_value": 28.118672703, "max_value": 30.260177601, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "c6b629cc573d7af57db899913407e050", "metric_id": "ef1b26bb3ca9d2f29daff336d09382c8", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "standard_deviation", "anomaly_score": 3.81630247, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 31.186897872, "min_metric_value": 27.738132373, "max_metric_value": 30.773882779, "training_avg": 29.256007576, "training_stddev": 0.505958401, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last standard_deviation value is 31.187. The average for this metric is 29.256.", "is_anomalous": true}], "result_description": "In column NULL_COUNT_INT, the last standard_deviation value is 31.187. The average for this metric is 29.256."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "max", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('max' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_INT, the last max value is 198. The average for this metric is 199.933.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('max' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Max", "metrics": [{"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "76bc3635b42fc9ddf2de14955f81a0fe", "metric_id": "1f7e35dd5e3eff890a950f9641b9c649", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "9b818742a0883e9210b7dbf064b06b40", "metric_id": "449678b0241f29f1eb8ee584ab448a05", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "ce4f1509e2514bdc46b2e753bd49ebcb", "metric_id": "8346318196bbfb739e0d9a53b7654b70", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "a1549ef34c02ff447c24f36b0870ce18", "metric_id": "11e672bb18ac986ca4dde742921223b3", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "21484265b323066ab1f0842b039e300b", "metric_id": "79510a0bac5321d46488be065130fc0b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "c03d0317a8870a8180e97e8bd4786e83", "metric_id": "a1562fe1749f369f6540710a71e5fbc2", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "039ab17aea896089be9c3a7482624c25", "metric_id": "153b352f7b223cb12f5728989c36edac", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "a14e79f57351937afd169e7b0ea04a7d", "metric_id": "86619b02c20faee1511d340253c66766", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "3793fbaa260d29178c01a9524a5c7b49", "metric_id": "8fd9827f47c6ec7ccf013c6f64a57cde", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "e2c8b950f0576440853d0530bb2a8ffd", "metric_id": "66a6656508941ab04b7a758240d657d5", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "559ebd0ec85ff0d2ab4aecb5fce2f7e0", "metric_id": "c21cdefd160a6c931f18f0b3b1316480", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "7625269c994711115ec0e1221d627434", "metric_id": "76ffebfcd08f66cb308c4fe34f901c6a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "6914fbba471def3fc3d81f3b789be5ed", "metric_id": "4eede51a140bb2ffb0a83e84f9fcf97d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "9cdae7a0fe2f32081ef4270bd516396c", "metric_id": "560bbe90784e9360475e9013308c5059", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "abc2e2a7c323e20846ed44cf621f9565", "metric_id": "dce092f160385b2bdd96efb2c6f0925c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "121bea9906aac2d29b2fedcc37b8d5b9", "metric_id": "f2011ae7f921885557fb72b4cf4ab496", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "5dbbda030ed09cdfa228b327f5476778", "metric_id": "4247e6da1bb87ace601980452fd53162", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "0fdc292ce9b1f0db8245eff1e253da98", "metric_id": "25051706ed50683a89e8a7fc69932819", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "f6c63f55f2d66fb3661f5c68f6867bc6", "metric_id": "85861c961e80ff0204de2db74d5acd38", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "e8605aa897200b5fc647f223dfebf078", "metric_id": "be4f317f630cbed789bdf50e885bb013", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "81bf134163e3145c8f3993c4b0ab801a", "metric_id": "1200f2aa2036bb570817586cf86b5786", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "fea0a352e813fa329871e0f237c77bf3", "metric_id": "28f5014827d544f3da8015ecdcd8c603", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "d241657bab8aa4dc3e502147d02b0b6b", "metric_id": "e9a074337216a7fd78a780659d1230e3", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "239054583c3204b8c4c0ed70b4284c8b", "metric_id": "f68f84e0de24fdad390bedb612eaa72d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "3203c444d5ec6a7c27e22edfc68cce65", "metric_id": "7b5ae9e65b4df172ba0b8cb4fff0c9ae", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "38b19b089029cf713649add12040e685", "metric_id": "8382b20a128be6202c4fe6a56912cdf9", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "fac58507687952446610e27659d0a8ac", "metric_id": "b5011258381a7500a786f4fe55d3407c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 200.0, "average": 200.0, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "4fa42a18a7ee5a4c008b9f67ec9cee47", "metric_id": "6d52eeb389ba820ec979467382f7a50e", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "max", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 200.0, "min_metric_value": 200.0, "max_metric_value": 200.0, "training_avg": 200.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last max value is 200. The average for this metric is 200.", "is_anomalous": false}, {"value": 198.0, "average": 199.933333333, "min_value": 200.0, "max_value": 200.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "778669754bdc13a35202023c23f21f47", "metric_id": "ce40017992ecf99973d9de89030b1449", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "max", "anomaly_score": -5.294651389, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 198.0, "min_metric_value": 198.837888218, "max_metric_value": 201.028778448, "training_avg": 199.933333333, "training_stddev": 0.3651483717, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last max value is 198. The average for this metric is 199.933.", "is_anomalous": true}], "result_description": "In column NULL_COUNT_INT, the last max value is 198. The average for this metric is 199.933."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_BOOL", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_BOOL' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_BOOL, the last null_percent value is 58.333. The average for this metric is 21.131.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_BOOL' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Null Percent", "metrics": [{"value": 18.167, "average": 19.5, "min_value": 13.844559964, "max_value": 25.155440036, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "d4dcb556b387f3e4c6fb061181f1e32c", "metric_id": "b86147a7a35e485f3a275a873006504c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_percent", "anomaly_score": -0.7071067812, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 18.167, "min_metric_value": 13.844559964, "max_metric_value": 25.155440036, "training_avg": 19.5, "training_stddev": 1.885146679, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_percent value is 18.167. The average for this metric is 19.5.", "is_anomalous": false}, {"value": 20.722, "average": 19.907333333, "min_value": 15.382750148, "max_value": 24.431916519, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "e843935dc5947b6a62ca70e361685eb4", "metric_id": "573f705db0481a33f0dad1dc41107b31", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_percent", "anomaly_score": 0.5401602534, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 20.722, "min_metric_value": 15.382750148, "max_metric_value": 24.431916519, "training_avg": 19.907333333, "training_stddev": 1.508194395, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_percent value is 20.722. The average for this metric is 19.907.", "is_anomalous": false}, {"value": 20.889, "average": 20.15275, "min_value": 16.175796612, "max_value": 24.129703388, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "9a2222a4671a2f4d07c42765e0e90a0c", "metric_id": "d7693340a39bdfc0f96e693333bea31a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_percent", "anomaly_score": 0.5553874498, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 20.889, "min_metric_value": 16.175796612, "max_metric_value": 24.129703388, "training_avg": 20.15275, "training_stddev": 1.325651129, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_percent value is 20.889. The average for this metric is 20.153.", "is_anomalous": false}, {"value": 21.444, "average": 20.411, "min_value": 16.555704097, "max_value": 24.266295903, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "25191c62161afc27a578a452ab965e4d", "metric_id": "41bff1e52e91865f1940188886d8f5e6", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_percent", "anomaly_score": 0.8038293501, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 21.444, "min_metric_value": 16.555704097, "max_metric_value": 24.266295903, "training_avg": 20.411, "training_stddev": 1.285098634, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_percent value is 21.444. The average for this metric is 20.411.", "is_anomalous": false}, {"value": 19.944, "average": 20.333166667, "min_value": 16.83777274, "max_value": 23.828560593, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "6e693f5b4495fba576ef6c8905a83e38", "metric_id": "5bd003eb912cb1532657b2dfdc6e7dbd", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_percent", "anomaly_score": -0.3340109941, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 19.944, "min_metric_value": 16.83777274, "max_metric_value": 23.828560593, "training_avg": 20.333166667, "training_stddev": 1.165131309, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_percent value is 19.944. The average for this metric is 20.333.", "is_anomalous": false}, {"value": 18.944, "average": 20.134714286, "min_value": 16.576254908, "max_value": 23.693173663, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "c097e8e82ed38ef5c9434a3d4ee14c1c", "metric_id": "e1da17e5e79bbd4b97d9eb5d48f16cf2", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_percent", "anomaly_score": -1.003845338, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 18.944, "min_metric_value": 16.576254908, "max_metric_value": 23.693173663, "training_avg": 20.134714286, "training_stddev": 1.186153126, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_percent value is 18.944. The average for this metric is 20.135.", "is_anomalous": false}, {"value": 19.333, "average": 20.0345, "min_value": 16.632034046, "max_value": 23.436965954, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "2bba59eb41592d7bea3345f3a366377c", "metric_id": "368b0f9df5779e0cbec06c544a27616f", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_percent", "anomaly_score": -0.6185219862, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 19.333, "min_metric_value": 16.632034046, "max_metric_value": 23.436965954, "training_avg": 20.0345, "training_stddev": 1.134155318, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_percent value is 19.333. The average for this metric is 20.035.", "is_anomalous": false}, {"value": 21.278, "average": 20.172666667, "min_value": 16.755654814, "max_value": 23.589678519, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "a8267004fd0acf74cfb44875764091a7", "metric_id": "71e27eed232cc59a3f617cf246162c0a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_percent", "anomaly_score": 0.970438542, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 21.278, "min_metric_value": 16.755654814, "max_metric_value": 23.589678519, "training_avg": 20.172666667, "training_stddev": 1.139003951, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_percent value is 21.278. The average for this metric is 20.173.", "is_anomalous": false}, {"value": 18.611, "average": 20.0165, "min_value": 16.470578047, "max_value": 23.562421953, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "4699546f0e49e74e0688657b8be31161", "metric_id": "3cc32260c3d022f1c5e8fc22a12a350e", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_percent", "anomaly_score": -1.189112466, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 18.611, "min_metric_value": 16.470578047, "max_metric_value": 23.562421953, "training_avg": 20.0165, "training_stddev": 1.181973984, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_percent value is 18.611. The average for this metric is 20.017.", "is_anomalous": false}, {"value": 20.167, "average": 20.030181818, "min_value": 16.663471514, "max_value": 23.396892122, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "05928587e8d3a95c2cbf7f1d21088771", "metric_id": "3b809a3089b114e6a2b3f5e91d7ca557", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_percent", "anomaly_score": 0.1219156115, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 20.167, "min_metric_value": 16.663471514, "max_metric_value": 23.396892122, "training_avg": 20.030181818, "training_stddev": 1.122236768, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_percent value is 20.167. The average for this metric is 20.03.", "is_anomalous": false}, {"value": 19.0, "average": 19.944333333, "min_value": 16.612627546, "max_value": 23.276039121, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "afba0383dcfb9afcad64f10a014926d7", "metric_id": "4d484f4c47fb938fa7fb6d4fb33cc53f", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_percent", "anomaly_score": -0.8503151781, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 19.0, "min_metric_value": 16.612627546, "max_metric_value": 23.276039121, "training_avg": 19.944333333, "training_stddev": 1.110568596, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_percent value is 19. The average for this metric is 19.944.", "is_anomalous": false}, {"value": 21.111, "average": 20.034076923, "min_value": 16.699778017, "max_value": 23.368375829, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "bf5b160045e1e4f1be0ccd4792e5a58f", "metric_id": "b623925d7889e03c8bf6b5a599efb9e9", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_percent", "anomaly_score": 0.9689500918, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 21.111, "min_metric_value": 16.699778017, "max_metric_value": 23.368375829, "training_avg": 20.034076923, "training_stddev": 1.111432969, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_percent value is 21.111. The average for this metric is 20.034.", "is_anomalous": false}, {"value": 18.833, "average": 19.948285714, "min_value": 16.603180384, "max_value": 23.293391045, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "b184b30ad2c2602b808adf446ce67985", "metric_id": "7dd371e7c29aa74955b658868a15d8b2", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_percent", "anomaly_score": -1.00022475, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 18.833, "min_metric_value": 16.603180384, "max_metric_value": 23.293391045, "training_avg": 19.948285714, "training_stddev": 1.11503511, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_percent value is 18.833. The average for this metric is 19.948.", "is_anomalous": false}, {"value": 18.444, "average": 19.848, "min_value": 16.420436934, "max_value": 23.275563066, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "1dfad2e76e3618d2c577ebaa3626dfa1", "metric_id": "f8116790f234d12ae6905732ecd66fc3", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_percent", "anomaly_score": -1.228861415, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 18.444, "min_metric_value": 16.420436934, "max_metric_value": 23.275563066, "training_avg": 19.848, "training_stddev": 1.142521022, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_percent value is 18.444. The average for this metric is 19.848.", "is_anomalous": false}, {"value": 19.722, "average": 19.840125, "min_value": 16.527436326, "max_value": 23.152813674, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "77bc2efd7da33028be3b76001dc9485a", "metric_id": "7b16e4d6ace184433c05e9c07cd0e4e4", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_percent", "anomaly_score": -0.106975039, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 19.722, "min_metric_value": 16.527436326, "max_metric_value": 23.152813674, "training_avg": 19.840125, "training_stddev": 1.104229558, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_percent value is 19.722. The average for this metric is 19.84.", "is_anomalous": false}, {"value": 19.667, "average": 19.829941176, "min_value": 16.619971585, "max_value": 23.039910768, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "b1c1df9c56ca44bf5b909074d0c5d8f1", "metric_id": "fdad4b7fb942707288e85c4705fe4184", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_percent", "anomaly_score": -0.1522829159, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 19.667, "min_metric_value": 16.619971585, "max_metric_value": 23.039910768, "training_avg": 19.829941176, "training_stddev": 1.069989864, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_percent value is 19.667. The average for this metric is 19.83.", "is_anomalous": false}, {"value": 19.778, "average": 19.827055556, "min_value": 16.712711054, "max_value": 22.941400057, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "9e202857fa2bee80ff17d913e60faf77", "metric_id": "08e625a5d57a7c4c7a11902f2893b9fd", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_percent", "anomaly_score": -0.04725445967, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 19.778, "min_metric_value": 16.712711054, "max_metric_value": 22.941400057, "training_avg": 19.827055556, "training_stddev": 1.038114834, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_percent value is 19.778. The average for this metric is 19.827.", "is_anomalous": false}, {"value": 19.5, "average": 19.809842105, "min_value": 16.774884402, "max_value": 22.844799809, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "4484119678f892a65618a83b24ae41c8", "metric_id": "e4bc64798e8036b24ff30b9ada8865b3", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_percent", "anomaly_score": -0.3062732356, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 19.5, "min_metric_value": 16.774884402, "max_metric_value": 22.844799809, "training_avg": 19.809842105, "training_stddev": 1.011652568, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_percent value is 19.5. The average for this metric is 19.81.", "is_anomalous": false}, {"value": 19.389, "average": 19.7888, "min_value": 16.821329849, "max_value": 22.756270151, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "46e00a38f93fa2b06e18d87c44ae4814", "metric_id": "5a1a7080877846a6b5467fe71413143b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_percent", "anomaly_score": -0.4041826671, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 19.389, "min_metric_value": 16.821329849, "max_metric_value": 22.756270151, "training_avg": 19.7888, "training_stddev": 0.9891567169, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_percent value is 19.389. The average for this metric is 19.789.", "is_anomalous": false}, {"value": 20.0, "average": 19.798857143, "min_value": 16.903222194, "max_value": 22.694492092, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "eb2b98ce01b6dcea5e04e8aba499514e", "metric_id": "b8d5b4472dfd54e6d00c21dc27032bef", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_percent", "anomaly_score": 0.208392488, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 20.0, "min_metric_value": 16.903222194, "max_metric_value": 22.694492092, "training_avg": 19.798857143, "training_stddev": 0.9652116496, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_percent value is 20. The average for this metric is 19.799.", "is_anomalous": false}, {"value": 19.278, "average": 19.775181818, "min_value": 16.929762095, "max_value": 22.620601542, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "6abe62b75f95612415e0983fded775cd", "metric_id": "1ea2d1eff790b8499344a44d497b3a74", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_percent", "anomaly_score": -0.5241917184, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 19.278, "min_metric_value": 16.929762095, "max_metric_value": 22.620601542, "training_avg": 19.775181818, "training_stddev": 0.9484732411, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_percent value is 19.278. The average for this metric is 19.775.", "is_anomalous": false}, {"value": 17.889, "average": 19.693173913, "min_value": 16.673152226, "max_value": 22.7131956, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "89eedf9347dec08832a2e6056026eda1", "metric_id": "92bc3b7c89e1765a1d9269482927ce79", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_percent", "anomaly_score": -1.792212872, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 17.889, "min_metric_value": 16.673152226, "max_metric_value": 22.7131956, "training_avg": 19.693173913, "training_stddev": 1.006673896, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_percent value is 17.889. The average for this metric is 19.693.", "is_anomalous": false}, {"value": 20.333, "average": 19.719833333, "min_value": 16.740319498, "max_value": 22.699347169, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "81f8fbbb11fe218b0ae20239afeccc10", "metric_id": "a1f4e05028d78a5a4643033efd64a1fd", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_percent", "anomaly_score": 0.6173826005, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 20.333, "min_metric_value": 16.740319498, "max_metric_value": 22.699347169, "training_avg": 19.719833333, "training_stddev": 0.9931712785, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_percent value is 20.333. The average for this metric is 19.72.", "is_anomalous": false}, {"value": 21.389, "average": 19.7866, "min_value": 16.702672472, "max_value": 22.870527528, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "34bf1041e3f3df2c2dee1caaa65396e8", "metric_id": "636298df34dcc93e6328fff01ca6afbf", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_percent", "anomaly_score": 1.558791494, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 21.389, "min_metric_value": 16.702672472, "max_metric_value": 22.870527528, "training_avg": 19.7866, "training_stddev": 1.027975843, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_percent value is 21.389. The average for this metric is 19.787.", "is_anomalous": false}, {"value": 19.556, "average": 19.777730769, "min_value": 16.753066847, "max_value": 22.802394691, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "f0beeb896ad56770fb4e35319abdb095", "metric_id": "f2dc3b983b0011c896e38918233c895f", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_percent", "anomaly_score": -0.219922717, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 19.556, "min_metric_value": 16.753066847, "max_metric_value": 22.802394691, "training_avg": 19.777730769, "training_stddev": 1.008221307, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_percent value is 19.556. The average for this metric is 19.778.", "is_anomalous": false}, {"value": 19.167, "average": 19.755111111, "min_value": 16.768297823, "max_value": 22.741924399, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "2f4b25fb1ff8cf1809f3c5a5f56b44bc", "metric_id": "4b6f4c5fff973552e5b9566172d298d4", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_percent", "anomaly_score": -0.5907076082, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 19.167, "min_metric_value": 16.768297823, "max_metric_value": 22.741924399, "training_avg": 19.755111111, "training_stddev": 0.9956044292, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_percent value is 19.167. The average for this metric is 19.755.", "is_anomalous": false}, {"value": 18.889, "average": 19.724178571, "min_value": 16.752350284, "max_value": 22.696006859, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "b90eef631b2cbe0509e380db44df5e38", "metric_id": "a45c7b6918372b75bac43201420186c2", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_percent", "anomaly_score": -0.8430957216, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 18.889, "min_metric_value": 16.752350284, "max_metric_value": 22.696006859, "training_avg": 19.724178571, "training_stddev": 0.9906094291, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_percent value is 18.889. The average for this metric is 19.724.", "is_anomalous": false}, {"value": 23.333, "average": 19.84862069, "min_value": 16.304872999, "max_value": 23.39236838, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "f6b0f321e709e20c780b13aa209cedae", "metric_id": "dfe52690f6c27eda4ac2ea5382deb063", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_percent", "anomaly_score": 2.949741021, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 23.333, "min_metric_value": 16.304872999, "max_metric_value": 23.39236838, "training_avg": 19.84862069, "training_stddev": 1.18124923, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_percent value is 23.333. The average for this metric is 19.849.", "is_anomalous": false}, {"value": 58.333, "average": 21.131433333, "min_value": 16.304872999, "max_value": 23.39236838, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "9bd790ff62438df872d0882993bdcabd", "metric_id": "5a1bf0cfd8092f90f569610f9ab91751", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_BOOL", "metric_name": "null_percent", "anomaly_score": 5.223853013, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 58.333, "min_metric_value": -0.2330077637, "max_metric_value": 42.49587443, "training_avg": 21.131433333, "training_stddev": 7.121480366, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_BOOL, the last null_percent value is 58.333. The average for this metric is 21.131.", "is_anomalous": true}], "result_description": "In column NULL_PERCENT_BOOL, the last null_percent value is 58.333. The average for this metric is 21.131."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "zero_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('zero_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_INT, the last zero_percent value is 80. The average for this metric is 5.567.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('zero_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Zero Percent", "metrics": [{"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "affa5096271e5ec1ab3ee92cbae8a5b0", "metric_id": "9dd69682bdaac3bd261cab063330f3f7", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "cb488f4ed77e51441f53238689dfe2d8", "metric_id": "18043d219834cdf0eb46709766810cbb", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "9791787d8067801fcaf24f81cf0caaff", "metric_id": "f81d71d395861c6a4832a918363e730f", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "941173437538e40125ac200f47b70094", "metric_id": "19f1959038bec99d8da2713b200265a8", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "a849b575bb4db884771fdd626924eafd", "metric_id": "9155140e2894d050bbf604405c949abd", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "d5874063b838cf2978096181a1026382", "metric_id": "f745da683c30f4e0adb714f24c49836e", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "789f09a028f08a68f4220829dc824058", "metric_id": "6779ec9a387602e32de362923e2216c9", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "cbf7962f6fd6b2818aca557a96ee470a", "metric_id": "adb1cd5ee3a88db500cf302b99b97f98", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "41579b7bc38dad55432c3c692207c1e9", "metric_id": "09f56d8229723e63e6fb6201decded2b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "c07e715f99276d02f7416fc72550aabb", "metric_id": "4cda762068c9d0e24ab72a50cef47090", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "d5e808d5913feea298f00679b6534d05", "metric_id": "6c832935112281610d2104c445141ae4", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "5d87a39f118a158a186bc0b7ba6e27ce", "metric_id": "fcaf89fd13d8b2b10f0d426818da5e7a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "f1a25bf10b1a571e728acfe047066997", "metric_id": "e6537d28bdcf2f8651cabc4aeeeeca41", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "dd923f0071da9e7e76f0a12195b3a0ab", "metric_id": "d0b37a81f3a1e9bf74093a2e47979c17", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "06e4f2c961db70ac22e3d9847c58bc44", "metric_id": "157592dea732cd2137cf79a8fcf6b262", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "d4437d810cb02ec45946aa359d269d63", "metric_id": "6c424c365b8cc5e11829cdb47c6a2f53", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "08240a8ce3d38bcbcde4691fab4953b4", "metric_id": "30fccc2905c10ccacce8070f021afff7", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "2f65b26d4913bfb7d062854b91450ebc", "metric_id": "af4e1650b45959b24b1b7280e217ce15", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "f46486c7eadc17d8ef1914bfb7e712df", "metric_id": "3b4c7bd6b5f6366d30e0d5cf60ace6d6", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "92f15ba1398519f564d4879a5ee58aa9", "metric_id": "9392ba9faa2bd4ac8e84a287a8f06c33", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "4a868ec4f4a15c032d4db04d3b8360bb", "metric_id": "67246fe6a6cb80780718d7e7b75db227", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "1cd850e3bbff2e4b4c18f84cd7191569", "metric_id": "f3f1ca76ded7f8046f3bb05a6b7379b9", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "42cbf0544e8c737053c42a184a4bc534", "metric_id": "07ba697d09167f2889b9fefbec976c7d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "0099535669dd31e65192f93217cef36c", "metric_id": "0580523f1e444cbd8c410f9d62edb946", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "659a4fa4a7b49e6623d4d6367438b644", "metric_id": "6b998293067f3e0f78b85bacaecd473b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "ac5917c99c6a8a565d737adb794c3c20", "metric_id": "9d56b544f643a4aa1ed20709740300d5", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "0c30310f502bd9c15c4d41f0bf67eb77", "metric_id": "1e7cfaf7400d7ac521c12483ddc7345c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "4d4d78eeea6930705f1523d2e67e19c7", "metric_id": "1e775e59b04d1e273fc1e1104584b094", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 80.0, "average": 5.566666667, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "e1727ba2ed6b1f2d50a4cdc898c8f329", "metric_id": "56bf9d45997040642528957c38c5169d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_INT", "metric_name": "zero_percent", "anomaly_score": 5.294651389, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 80.0, "min_metric_value": -36.607970261, "max_metric_value": 47.741303595, "training_avg": 5.566666667, "training_stddev": 14.058212309, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_INT, the last zero_percent value is 80. The average for this metric is 5.567.", "is_anomalous": true}], "result_description": "In column NULL_COUNT_INT, the last zero_percent value is 80. The average for this metric is 5.567."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "zero_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('zero_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_FLOAT, the last zero_count value is 187. The average for this metric is 349.533.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('zero_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Zero Count", "metrics": [{"value": 386.0, "average": 389.5, "min_value": 374.650757595, "max_value": 404.349242405, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "104d00db1b9f83fd60b1d5e2f16aef05", "metric_id": "221c700b587a8e57ea19229c7e9934b8", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_count", "anomaly_score": -0.7071067812, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 386.0, "min_metric_value": 374.650757595, "max_metric_value": 404.349242405, "training_avg": 389.5, "training_stddev": 4.949747468, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_count value is 386. The average for this metric is 389.5.", "is_anomalous": false}, {"value": 347.0, "average": 375.333333333, "min_value": 300.976087553, "max_value": 449.690579113, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "3753d209a77ac294b6a5a7f20eb2cac2", "metric_id": "099bba64679c3e116f8cc1e2b8977581", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_count", "anomaly_score": -1.143130022, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 347.0, "min_metric_value": 300.976087553, "max_metric_value": 449.690579113, "training_avg": 375.333333333, "training_stddev": 24.785748593, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_count value is 347. The average for this metric is 375.333.", "is_anomalous": false}, {"value": 363.0, "average": 372.25, "min_value": 308.781503878, "max_value": 435.718496122, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "0525cf811fe3fef0af8ded8e676452c1", "metric_id": "6565172f0cacd7c6fb69da10e0ee7f60", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_count", "anomaly_score": -0.4372247918, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 363.0, "min_metric_value": 308.781503878, "max_metric_value": 435.718496122, "training_avg": 372.25, "training_stddev": 21.156165374, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_count value is 363. The average for this metric is 372.25.", "is_anomalous": false}, {"value": 370.0, "average": 371.8, "min_value": 316.751839268, "max_value": 426.848160732, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "0a361ce081a5943b320d3357745dd2ed", "metric_id": "04825e147c919203cae9938ff14ba795", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_count", "anomaly_score": -0.09809592052, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 370.0, "min_metric_value": 316.751839268, "max_metric_value": 426.848160732, "training_avg": 371.8, "training_stddev": 18.349386911, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_count value is 370. The average for this metric is 371.8.", "is_anomalous": false}, {"value": 347.0, "average": 367.666666667, "min_value": 309.815132547, "max_value": 425.518200786, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "9776a5433d9b3c52f1dcbf6283196c4e", "metric_id": "f8802e3e5e5fe542ca1af4686400fd2d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_count", "anomaly_score": -1.071708831, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 347.0, "min_metric_value": 309.815132547, "max_metric_value": 425.518200786, "training_avg": 367.666666667, "training_stddev": 19.283844707, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_count value is 347. The average for this metric is 367.667.", "is_anomalous": false}, {"value": 367.0, "average": 367.571428571, "min_value": 314.755035013, "max_value": 420.38782213, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "dc8383bd7f1e258cfb34cc5f39d81d4e", "metric_id": "081a6d9f9da9b225a9be80d1bf72c81d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_count", "anomaly_score": -0.03245745494, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 367.0, "min_metric_value": 314.755035013, "max_metric_value": 420.38782213, "training_avg": 367.571428571, "training_stddev": 17.60546452, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_count value is 367. The average for this metric is 367.571.", "is_anomalous": false}, {"value": 343.0, "average": 364.5, "min_value": 309.089841055, "max_value": 419.910158945, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "a14838a8f3c5e0778290320bdf2171b9", "metric_id": "cdd9e901b18b563a910897c595025ab9", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_count", "anomaly_score": -1.164046471, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 343.0, "min_metric_value": 309.089841055, "max_metric_value": 419.910158945, "training_avg": 364.5, "training_stddev": 18.470052982, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_count value is 343. The average for this metric is 364.5.", "is_anomalous": false}, {"value": 380.0, "average": 366.222222222, "min_value": 312.122776754, "max_value": 420.321667691, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "901aa174bd9c36ad12cf8cc173119f92", "metric_id": "82d20da86a6a28228ccebc06ff0fbaca", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_count", "anomaly_score": 0.7640250834, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 380.0, "min_metric_value": 312.122776754, "max_metric_value": 420.321667691, "training_avg": 366.222222222, "training_stddev": 18.03314849, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_count value is 380. The average for this metric is 366.222.", "is_anomalous": false}, {"value": 393.0, "average": 368.9, "min_value": 311.91842403, "max_value": 425.88157597, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "e2da20a9f7742a7ec9bf9104dce4596b", "metric_id": "a5b913eb9652f9c1616ca7c1992556c1", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_count", "anomaly_score": 1.268831175, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 393.0, "min_metric_value": 311.91842403, "max_metric_value": 425.88157597, "training_avg": 368.9, "training_stddev": 18.993858657, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_count value is 393. The average for this metric is 368.9.", "is_anomalous": false}, {"value": 378.0, "average": 369.727272727, "min_value": 315.046712778, "max_value": 424.407832677, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "a254b9868df515e98c856b20703b06b0", "metric_id": "c877bbd23c55685a29bd3e8ffb62bf29", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_count", "anomaly_score": 0.4538757804, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 378.0, "min_metric_value": 315.046712778, "max_metric_value": 424.407832677, "training_avg": 369.727272727, "training_stddev": 18.226853316, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_count value is 378. The average for this metric is 369.727.", "is_anomalous": false}, {"value": 380.0, "average": 370.583333333, "min_value": 317.693868839, "max_value": 423.472797828, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "e19f7a11a71f08c485f315f61ab5d885", "metric_id": "f07cfe0ae6d2cee57f45397e4faab443", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_count", "anomaly_score": 0.5341328423, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 380.0, "min_metric_value": 317.693868839, "max_metric_value": 423.472797828, "training_avg": 370.583333333, "training_stddev": 17.629821498, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_count value is 380. The average for this metric is 370.583.", "is_anomalous": false}, {"value": 362.0, "average": 369.923076923, "min_value": 318.78412594, "max_value": 421.062027907, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "a13d2c7afc0a694341ffd8837e1bfd3a", "metric_id": "0d804cb83405449d2e7dc444430ec78d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_count", "anomaly_score": -0.4647969955, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 362.0, "min_metric_value": 318.78412594, "max_metric_value": 421.062027907, "training_avg": 369.923076923, "training_stddev": 17.046316994, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_count value is 362. The average for this metric is 369.923.", "is_anomalous": false}, {"value": 364.0, "average": 369.5, "min_value": 320.138305103, "max_value": 418.861694897, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "004d6d4028f0f1ca05cd47b83bb84a2a", "metric_id": "84f69a869a3897be5743a23f1219d72d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_count", "anomaly_score": -0.3342672903, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 364.0, "min_metric_value": 320.138305103, "max_metric_value": 418.861694897, "training_avg": 369.5, "training_stddev": 16.453898299, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_count value is 364. The average for this metric is 369.5.", "is_anomalous": false}, {"value": 383.0, "average": 370.4, "min_value": 321.697990655, "max_value": 419.102009345, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "22904c2d115294d9f043f55d711c6ab3", "metric_id": "352922db01d304a2d1e6d084c83138da", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_count", "anomaly_score": 0.7761486745, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 383.0, "min_metric_value": 321.697990655, "max_metric_value": 419.102009345, "training_avg": 370.4, "training_stddev": 16.234003115, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_count value is 383. The average for this metric is 370.4.", "is_anomalous": false}, {"value": 353.0, "average": 369.3125, "min_value": 320.485637516, "max_value": 418.139362484, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "98c4cec4edc043fb3041b51a60529cb9", "metric_id": "fd8a8e46276c2d53e16b4ad782eebcc7", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_count", "anomaly_score": -1.002265915, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 353.0, "min_metric_value": 320.485637516, "max_metric_value": 418.139362484, "training_avg": 369.3125, "training_stddev": 16.275620828, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_count value is 353. The average for this metric is 369.313.", "is_anomalous": false}, {"value": 341.0, "average": 367.647058824, "min_value": 316.077360128, "max_value": 419.216757519, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "167e067d5460fea5b32ff6769f7d99c2", "metric_id": "a2da1c1d30877a1a311d7c9344dbea38", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_count", "anomaly_score": -1.550157912, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 341.0, "min_metric_value": 316.077360128, "max_metric_value": 419.216757519, "training_avg": 367.647058824, "training_stddev": 17.189899565, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_count value is 341. The average for this metric is 367.647.", "is_anomalous": false}, {"value": 368.0, "average": 367.666666667, "min_value": 317.636087782, "max_value": 417.697245551, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "0a453459008bf0cdefaef6bc41f151ec", "metric_id": "0281be49e4e129bcd37407a1933e42c5", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_count", "anomaly_score": 0.01998777592, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 368.0, "min_metric_value": 317.636087782, "max_metric_value": 417.697245551, "training_avg": 367.666666667, "training_stddev": 16.676859628, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_count value is 368. The average for this metric is 367.667.", "is_anomalous": false}, {"value": 346.0, "average": 366.526315789, "min_value": 315.669964947, "max_value": 417.382666632, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "c6e517b65100fa09038d24ab363c015e", "metric_id": "348e817d9699eca4e589900f74ae1019", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_count", "anomaly_score": -1.210840856, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 346.0, "min_metric_value": 315.669964947, "max_metric_value": 417.382666632, "training_avg": 366.526315789, "training_stddev": 16.952116948, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_count value is 346. The average for this metric is 366.526.", "is_anomalous": false}, {"value": 330.0, "average": 364.7, "min_value": 309.467572454, "max_value": 419.932427546, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "6b658b52bc2a211fc548867dd697b58d", "metric_id": "993dc8042d2560d84edbbbcc35bb5284", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_count", "anomaly_score": -1.884762351, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 330.0, "min_metric_value": 309.467572454, "max_metric_value": 419.932427546, "training_avg": 364.7, "training_stddev": 18.410809182, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_count value is 330. The average for this metric is 364.7.", "is_anomalous": false}, {"value": 361.0, "average": 364.523809524, "min_value": 310.635432828, "max_value": 418.412186219, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "51161794de37876c1cd1cd3f5c2ef35c", "metric_id": "b2c4ff1f7e208d574c04897235df45c5", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_count", "anomaly_score": -0.196172704, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 361.0, "min_metric_value": 310.635432828, "max_metric_value": 418.412186219, "training_avg": 364.523809524, "training_stddev": 17.962792232, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_count value is 361. The average for this metric is 364.524.", "is_anomalous": false}, {"value": 341.0, "average": 363.454545455, "min_value": 308.75489328, "max_value": 418.154197629, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "945ca4f773d61f353bcb62d378314f64", "metric_id": "df6b22dda9b2137ef7b2b1afe63804cc", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_count", "anomaly_score": -1.231518551, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 341.0, "min_metric_value": 308.75489328, "max_metric_value": 418.154197629, "training_avg": 363.454545455, "training_stddev": 18.233217392, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_count value is 341. The average for this metric is 363.455.", "is_anomalous": false}, {"value": 348.0, "average": 362.782608696, "min_value": 308.473218334, "max_value": 417.091999057, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "5357d12a894d411078ea9570414eb2de", "metric_id": "3b00b6528a885b713619452597414e75", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_count", "anomaly_score": -0.8165774978, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 348.0, "min_metric_value": 308.473218334, "max_metric_value": 417.091999057, "training_avg": 362.782608696, "training_stddev": 18.10313012, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_count value is 348. The average for this metric is 362.783.", "is_anomalous": false}, {"value": 381.0, "average": 363.541666667, "min_value": 309.267153071, "max_value": 417.816180263, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "a30b961eb778d93b78109cea06f48f0f", "metric_id": "6dcff41eb806616befbb5dd6bc5396ba", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_count", "anomaly_score": 0.9650017389, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 381.0, "min_metric_value": 309.267153071, "max_metric_value": 417.816180263, "training_avg": 363.541666667, "training_stddev": 18.091504532, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_count value is 381. The average for this metric is 363.542.", "is_anomalous": false}, {"value": 370.0, "average": 363.8, "min_value": 310.527117593, "max_value": 417.072882407, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "42d6ac35845d0c52ed5b0482618c2c4f", "metric_id": "6a19eb6a1a0c41e2e2b8424b683e8f5d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_count", "anomaly_score": 0.3491457409, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 370.0, "min_metric_value": 310.527117593, "max_metric_value": 417.072882407, "training_avg": 363.8, "training_stddev": 17.757627469, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_count value is 370. The average for this metric is 363.8.", "is_anomalous": false}, {"value": 361.0, "average": 363.692307692, "min_value": 311.46976614, "max_value": 415.914849244, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "704fa8c2c99f50816e2bc9e2c15a6e5b", "metric_id": "190ad8230aac01c3e28e801a5da3b17b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_count", "anomaly_score": -0.1546635387, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 361.0, "min_metric_value": 311.46976614, "max_metric_value": 415.914849244, "training_avg": 363.692307692, "training_stddev": 17.407513851, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_count value is 361. The average for this metric is 363.692.", "is_anomalous": false}, {"value": 376.0, "average": 364.148148148, "min_value": 312.449068158, "max_value": 415.847228138, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "8c1a2aeb2e55afc3887062db3ac7b2e3", "metric_id": "93c7437977df8efc1df281d48f217c33", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_count", "anomaly_score": 0.6877405858, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 376.0, "min_metric_value": 312.449068158, "max_metric_value": 415.847228138, "training_avg": 364.148148148, "training_stddev": 17.233026663, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_count value is 376. The average for this metric is 364.148.", "is_anomalous": false}, {"value": 391.0, "average": 365.107142857, "min_value": 312.139610124, "max_value": 418.07467559, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "cf5f0b4f837747b07ea23f814a2ad808", "metric_id": "cd96938fb127602087eb85ad18bd1dab", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_count", "anomaly_score": 1.466531806, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 391.0, "min_metric_value": 312.139610124, "max_metric_value": 418.07467559, "training_avg": 365.107142857, "training_stddev": 17.655844244, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_count value is 391. The average for this metric is 365.107.", "is_anomalous": false}, {"value": 76.0, "average": 355.137931034, "min_value": 312.139610124, "max_value": 418.07467559, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "06c6902ea5042667aeccaca775cba82f", "metric_id": "125bc18ae852b03e5a5fc44fea2b6f00", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_count", "anomaly_score": -4.947849709, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 76.0, "min_metric_value": 185.889905673, "max_metric_value": 524.385956396, "training_avg": 355.137931034, "training_stddev": 56.416008454, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_count value is 76. The average for this metric is 355.138.", "is_anomalous": true}, {"value": 187.0, "average": 349.533333333, "min_value": 159.432706622, "max_value": 539.633960044, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "3a527cb128ec004eaaaf8d7fa946731d", "metric_id": "9ff2e8686581ffcd8450e29bcd440908", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_FLOAT", "metric_name": "zero_count", "anomaly_score": -2.564957351, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 187.0, "min_metric_value": 159.432706622, "max_metric_value": 539.633960044, "training_avg": 349.533333333, "training_stddev": 63.36687557, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_FLOAT, the last zero_count value is 187. The average for this metric is 349.533.", "is_anomalous": false}], "result_description": "In column NULL_PERCENT_FLOAT, the last zero_count value is 187. The average for this metric is 349.533."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_STR, the last null_percent value is 58.333. The average for this metric is 21.43.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Null Percent", "metrics": [{"value": 19.167, "average": 20.6945, "min_value": 14.21386635, "max_value": 27.17513365, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "0c20a63f2d37ee962b9299d60f843460", "metric_id": "72c22806b17f6ce8f83f965f81b819ae", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_percent", "anomaly_score": -0.7071067812, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 19.167, "min_metric_value": 14.21386635, "max_metric_value": 27.17513365, "training_avg": 20.6945, "training_stddev": 2.160211217, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_percent value is 19.167. The average for this metric is 20.695.", "is_anomalous": false}, {"value": 20.278, "average": 20.555666667, "min_value": 15.916731121, "max_value": 25.194602213, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "a4f4c5876f09d2ab46183b4f502d8c88", "metric_id": "05416205229a1e0c5eea6146c0d0e473", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_percent", "anomaly_score": -0.1795670562, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 20.278, "min_metric_value": 15.916731121, "max_metric_value": 25.194602213, "training_avg": 20.555666667, "training_stddev": 1.546311849, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_percent value is 20.278. The average for this metric is 20.556.", "is_anomalous": false}, {"value": 19.556, "average": 20.30575, "min_value": 16.232056068, "max_value": 24.379443932, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "a8c96e78ee90458fb99719477c07eda7", "metric_id": "ebdcf791affec3dee7d4a1086f897fa1", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_percent", "anomaly_score": -0.5521401553, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 19.556, "min_metric_value": 16.232056068, "max_metric_value": 24.379443932, "training_avg": 20.30575, "training_stddev": 1.357897977, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_percent value is 19.556. The average for this metric is 20.306.", "is_anomalous": false}, {"value": 21.056, "average": 20.4558, "min_value": 16.787092898, "max_value": 24.124507102, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "9cfb016590a6e411ed84ede24301376a", "metric_id": "f3d5064de8bfef218955e8dc004dbdf3", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_percent", "anomaly_score": 0.490799606, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 21.056, "min_metric_value": 16.787092898, "max_metric_value": 24.124507102, "training_avg": 20.4558, "training_stddev": 1.222902367, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_percent value is 21.056. The average for this metric is 20.456.", "is_anomalous": false}, {"value": 20.889, "average": 20.528, "min_value": 17.20399296, "max_value": 23.85200704, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "6f1d2ea9b2242368e3868389a8b9eaa3", "metric_id": "bba87358fb277990c0e4f7bddc60d232", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_percent", "anomaly_score": 0.3258115844, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 20.889, "min_metric_value": 17.20399296, "max_metric_value": 23.85200704, "training_avg": 20.528, "training_stddev": 1.108002347, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_percent value is 20.889. The average for this metric is 20.528.", "is_anomalous": false}, {"value": 19.389, "average": 20.365285714, "min_value": 17.067482939, "max_value": 23.663088489, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "ba88864abb3d4ff56b9385285ea336e6", "metric_id": "5753b25e87d50fff71180968490f5201", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_percent", "anomaly_score": -0.8881238032, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 19.389, "min_metric_value": 17.067482939, "max_metric_value": 23.663088489, "training_avg": 20.365285714, "training_stddev": 1.099267592, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_percent value is 19.389. The average for this metric is 20.365.", "is_anomalous": false}, {"value": 21.278, "average": 20.479375, "min_value": 17.276401674, "max_value": 23.682348326, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "979b18bf45a4ef0f74d6983d3ead9760", "metric_id": "e1eaa7033fae9ff33f72b50ddf676398", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_percent", "anomaly_score": 0.7480159078, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 21.278, "min_metric_value": 17.276401674, "max_metric_value": 23.682348326, "training_avg": 20.479375, "training_stddev": 1.067657775, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_percent value is 21.278. The average for this metric is 20.479.", "is_anomalous": false}, {"value": 18.889, "average": 20.302666667, "min_value": 16.910624362, "max_value": 23.694708972, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "af1f72ca0092f562bcd5d49d1dfd139d", "metric_id": "8c7bfa144ec7165f0ab790e5a715984b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_percent", "anomaly_score": -1.250279218, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 18.889, "min_metric_value": 16.910624362, "max_metric_value": 23.694708972, "training_avg": 20.302666667, "training_stddev": 1.130680768, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_percent value is 18.889. The average for this metric is 20.303.", "is_anomalous": false}, {"value": 18.833, "average": 20.1557, "min_value": 16.666940894, "max_value": 23.644459106, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "5c23f6339f3a07a16b3e3bf0b9c64778", "metric_id": "489b577bbd98244b097b3c01bd4f9af1", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_percent", "anomaly_score": -1.137395813, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 18.833, "min_metric_value": 16.666940894, "max_metric_value": 23.644459106, "training_avg": 20.1557, "training_stddev": 1.162919702, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_percent value is 18.833. The average for this metric is 20.156.", "is_anomalous": false}, {"value": 20.556, "average": 20.192090909, "min_value": 16.862616233, "max_value": 23.521565585, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "5a288efe7378fecf968da834d721e733", "metric_id": "4fa91b7719feb54722657e0dbeb45ded", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_percent", "anomaly_score": 0.3278977553, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 20.556, "min_metric_value": 16.862616233, "max_metric_value": 23.521565585, "training_avg": 20.192090909, "training_stddev": 1.109824892, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_percent value is 20.556. The average for this metric is 20.192.", "is_anomalous": false}, {"value": 20.389, "average": 20.2085, "min_value": 17.029393565, "max_value": 23.387606435, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "cc23a58b8934d1b7369b3647ee9d5407", "metric_id": "7001cc52b76fee548e3f0aba6ddf7d58", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_percent", "anomaly_score": 0.170330881, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 20.389, "min_metric_value": 17.029393565, "max_metric_value": 23.387606435, "training_avg": 20.2085, "training_stddev": 1.059702145, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_percent value is 20.389. The average for this metric is 20.209.", "is_anomalous": false}, {"value": 20.556, "average": 20.235230769, "min_value": 17.17776585, "max_value": 23.292695688, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "0ef4d7d25121e3621f37719d54047a19", "metric_id": "7ae3e270961509e169af4064d7e69dde", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_percent", "anomaly_score": 0.3147403872, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 20.556, "min_metric_value": 17.17776585, "max_metric_value": 23.292695688, "training_avg": 20.235230769, "training_stddev": 1.019154973, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_percent value is 20.556. The average for this metric is 20.235.", "is_anomalous": false}, {"value": 21.611, "average": 20.3335, "min_value": 17.195703033, "max_value": 23.471296967, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "327448e2b8b77a9caccb16e8c3135fbd", "metric_id": "48e9df1cc81d45d2145b9c66e0d4938b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_percent", "anomaly_score": 1.221398338, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 21.611, "min_metric_value": 17.195703033, "max_metric_value": 23.471296967, "training_avg": 20.3335, "training_stddev": 1.045932322, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_percent value is 21.611. The average for this metric is 20.333.", "is_anomalous": false}, {"value": 19.833, "average": 20.300133333, "min_value": 17.2517238, "max_value": 23.348542867, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "d18deca81e3c0c619f76ee6424c34dc6", "metric_id": "31bc8974c41febd205ab22056a1c31f1", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_percent", "anomaly_score": -0.4597151349, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 19.833, "min_metric_value": 17.2517238, "max_metric_value": 23.348542867, "training_avg": 20.300133333, "training_stddev": 1.016136511, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_percent value is 19.833. The average for this metric is 20.3.", "is_anomalous": false}, {"value": 19.556, "average": 20.253625, "min_value": 17.256166702, "max_value": 23.251083298, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "f0bd85415de3d5e45191977f1d7a4b51", "metric_id": "c5de7a03d2649b5f2480ad897a5209e1", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_percent", "anomaly_score": -0.6982165527, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 19.556, "min_metric_value": 17.256166702, "max_metric_value": 23.251083298, "training_avg": 20.253625, "training_stddev": 0.9991527661, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_percent value is 19.556. The average for this metric is 20.254.", "is_anomalous": false}, {"value": 20.222, "average": 20.251764706, "min_value": 17.349396971, "max_value": 23.154132441, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "123069cba053c399d3bbff6b4af54d37", "metric_id": "d397f253bb4b8d35ca1d080647a49676", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_percent", "anomaly_score": -0.03076595587, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 20.222, "min_metric_value": 17.349396971, "max_metric_value": 23.154132441, "training_avg": 20.251764706, "training_stddev": 0.9674559117, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_percent value is 20.222. The average for this metric is 20.252.", "is_anomalous": false}, {"value": 20.667, "average": 20.274833333, "min_value": 17.443855655, "max_value": 23.105811012, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "e28c51a50f22486ca2e06dffa9fe989b", "metric_id": "6f543590a0bf19605a7e98f9d00935ce", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_percent", "anomaly_score": 0.4155808111, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 20.667, "min_metric_value": 17.443855655, "max_metric_value": 23.105811012, "training_avg": 20.274833333, "training_stddev": 0.9436592263, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_percent value is 20.667. The average for this metric is 20.275.", "is_anomalous": false}, {"value": 19.556, "average": 20.237, "min_value": 17.441655477, "max_value": 23.032344523, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "049f71583786b28688e52d8d3fa9e7ba", "metric_id": "a160f9b944e7953ee345358e97388488", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_percent", "anomaly_score": -0.7308580332, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 19.556, "min_metric_value": 17.441655477, "max_metric_value": 23.032344523, "training_avg": 20.237, "training_stddev": 0.9317815075, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_percent value is 19.556. The average for this metric is 20.237.", "is_anomalous": false}, {"value": 19.722, "average": 20.21125, "min_value": 17.468615925, "max_value": 22.953884075, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "9eaffbb7d662e17a1975baeed4858148", "metric_id": "5aa9495a08be65cb62009e3cd40175a7", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_percent", "anomaly_score": -0.5351607104, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 19.722, "min_metric_value": 17.468615925, "max_metric_value": 22.953884075, "training_avg": 20.21125, "training_stddev": 0.9142113584, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_percent value is 19.722. The average for this metric is 20.211.", "is_anomalous": false}, {"value": 21.556, "average": 20.275285714, "min_value": 17.460867865, "max_value": 23.089703563, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "db8eaf3f7c7de5c027fcb05fe2cf3c22", "metric_id": "397f2d877db32aed09b1203f025a0f04", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_percent", "anomaly_score": 1.36516433, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 21.556, "min_metric_value": 17.460867865, "max_metric_value": 23.089703563, "training_avg": 20.275285714, "training_stddev": 0.938139283, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_percent value is 21.556. The average for this metric is 20.275.", "is_anomalous": false}, {"value": 20.056, "average": 20.265318182, "min_value": 17.515148828, "max_value": 23.015487536, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "bf12c194502b9dba6e005d7fe2379645", "metric_id": "115440108fc674511c03b3e8f2b30165", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_percent", "anomaly_score": -0.228333046, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 20.056, "min_metric_value": 17.515148828, "max_metric_value": 23.015487536, "training_avg": 20.265318182, "training_stddev": 0.9167231179, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_percent value is 20.056. The average for this metric is 20.265.", "is_anomalous": false}, {"value": 20.056, "average": 20.256217391, "min_value": 17.566090297, "max_value": 22.946344485, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "79d53282521f9c5afa6ccdcf7f886127", "metric_id": "5121d5b7dbfdc4664a2d3482a11f31f2", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_percent", "anomaly_score": -0.2232802217, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 20.056, "min_metric_value": 17.566090297, "max_metric_value": 22.946344485, "training_avg": 20.256217391, "training_stddev": 0.8967090314, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_percent value is 20.056. The average for this metric is 20.256.", "is_anomalous": false}, {"value": 20.222, "average": 20.254791667, "min_value": 17.623712027, "max_value": 22.885871306, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "f3a85a757db64c55345339b9444414a9", "metric_id": "cf86ebc961a4a3dd62390f05c3478381", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_percent", "anomaly_score": -0.03738959419, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 20.222, "min_metric_value": 17.623712027, "max_metric_value": 22.885871306, "training_avg": 20.254791667, "training_stddev": 0.8770265466, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_percent value is 20.222. The average for this metric is 20.255.", "is_anomalous": false}, {"value": 19.833, "average": 20.23792, "min_value": 17.649834571, "max_value": 22.826005429, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "15898fc023ebb17de91e4e7d872e11df", "metric_id": "207b30187685d145b35685e8f3c17b27", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_percent", "anomaly_score": -0.4693662683, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 19.833, "min_metric_value": 17.649834571, "max_metric_value": 22.826005429, "training_avg": 20.23792, "training_stddev": 0.8626951431, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_percent value is 19.833. The average for this metric is 20.238.", "is_anomalous": false}, {"value": 19.167, "average": 20.196730769, "min_value": 17.583829684, "max_value": 22.809631855, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "2950b901925a7bc5cbc684fcd5f2076f", "metric_id": "ef3d7bffda00e3483830cc052ac4fffc", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_percent", "anomaly_score": -1.182284444, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 19.167, "min_metric_value": 17.583829684, "max_metric_value": 22.809631855, "training_avg": 20.196730769, "training_stddev": 0.8709670284, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_percent value is 19.167. The average for this metric is 20.197.", "is_anomalous": false}, {"value": 19.222, "average": 20.16062963, "min_value": 17.537393939, "max_value": 22.78386532, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "d941a83862ba3f489a46cb7f2e42415e", "metric_id": "b500a0637e29b307e6a6929b8b0077bf", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_percent", "anomaly_score": -1.073441056, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 19.222, "min_metric_value": 17.537393939, "max_metric_value": 22.78386532, "training_avg": 20.16062963, "training_stddev": 0.8744118968, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_percent value is 19.222. The average for this metric is 20.161.", "is_anomalous": false}, {"value": 19.222, "average": 20.127107143, "min_value": 17.498478814, "max_value": 22.755735472, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "8e1df3baa69b44f53b44c8a48185a1cd", "metric_id": "0c6900bf77aa0194b167f676e90bb328", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_percent", "anomaly_score": -1.032980357, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 19.222, "min_metric_value": 17.498478814, "max_metric_value": 22.755735472, "training_avg": 20.127107143, "training_stddev": 0.8762094431, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_percent value is 19.222. The average for this metric is 20.127.", "is_anomalous": false}, {"value": 21.0, "average": 20.157206897, "min_value": 17.530540359, "max_value": 22.783873434, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "c7f9c95f093fa4d55ce1ae1390969768", "metric_id": "d46f94c52e66e84672ec41abe53b9be9", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_percent", "anomaly_score": 0.9625810029, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 21.0, "min_metric_value": 17.530540359, "max_metric_value": 22.783873434, "training_avg": 20.157206897, "training_stddev": 0.8755555126, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_percent value is 21. The average for this metric is 20.157.", "is_anomalous": false}, {"value": 58.333, "average": 21.429733333, "min_value": 17.530540359, "max_value": 22.783873434, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "63df086f8609df93786897f262870d4a", "metric_id": "4087223824c3d6ac72865f97447eb1e2", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_percent", "anomaly_score": 5.254771722, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 58.333, "min_metric_value": 0.3613014679, "max_metric_value": 42.498165199, "training_avg": 21.429733333, "training_stddev": 7.022810622, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_percent value is 58.333. The average for this metric is 21.43.", "is_anomalous": true}], "result_description": "In column NULL_PERCENT_STR, the last null_percent value is 58.333. The average for this metric is 21.43."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "min", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('min' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_INT, the last min value is 101. The average for this metric is 100.033.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('min' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Min", "metrics": [{"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "6035637d37fea4e01ebe8ab4a42ed85b", "metric_id": "418f5b1c0d39f65b5ad2e02efadcbab4", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "8b045d6d3d8b7fcbd69a862ef5293be1", "metric_id": "1dc4034ddc7010abdfc7ef38ad0555c6", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "56f1d938030efa0962fc113487e2e1a7", "metric_id": "5d238f966d5925daac7225b8b8ee67d2", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "7dd4b6b7b2c5963b62697484949d6970", "metric_id": "689685445f298addd40f93a4611964bd", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "c1d95421e5e4a25e221600cff1219ce7", "metric_id": "c8c02eb285cff260f5b21a54a0a58ca8", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "43deb28258e0a9f408273599928ba2ce", "metric_id": "490f6c341122880205fec0ca3a055dbe", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "b1d50a611df3f3732f1450dc4f072ab2", "metric_id": "23769a1a0324ffb8c8185117c07a471f", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "ae0d26cd9fce67811307c1be5f75f1e9", "metric_id": "d6349af8992ca2738fc5bceefee63714", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "18b70e25bc130f08e65d4560f50f97e7", "metric_id": "437b358c0022aab78b865b4139235c7a", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "abac34eb799112d4b034b812916f812f", "metric_id": "5ab12683330e93e9c053ae77cff2e21c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "d5a040e0be9facd4232d30901d84563c", "metric_id": "f35b608914a0ca7a239d81af2ee21222", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "9779f4cb255f88e0e9b40203f149ed9a", "metric_id": "90a28c62e5e8e249f5d3e1f8e36fcff6", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "b6bac37cca5c8ab963747bc6e7fcb4ae", "metric_id": "608accae514af0bf5c67d2a49399bf63", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "39c12641c0682b50548075f449deacc2", "metric_id": "5b416e669f2a13e1b142e3b203c644be", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "16deab41cc7029f96ccac02ed5dba021", "metric_id": "430b88cf747b9c2f5f6bc789f4101a72", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "93a429d2768f0e89c66b9234fc3e0416", "metric_id": "5ddab754df71c7460c350aed2a4ba062", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "dd29d51acad494610a9ddaef3c9223f8", "metric_id": "8da0d490c53803e1ee6f52f7aa96248d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "d0380369ae4960edd799b9b578167f7f", "metric_id": "56bfe0d097fbcf235db5d7cf35d1ed34", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "eed64bcb52deeedb03eddadd2cdc8dae", "metric_id": "77a8df5a46d95ed9e64b7571083a8a4d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "705e413c8fd5bac491020f63a0c31e71", "metric_id": "329084f86a8895c477d348145999b596", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "5eb382e23bcd89c4481aff31cd53ce84", "metric_id": "8992886d395fa0a5364ea254c934b92e", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "9cf72efcbdb77ce70c6bad22a55e979b", "metric_id": "5c7ab06c6e8e46a71c776c57dc75cdb4", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "01f1c87c67869f22e429002475265454", "metric_id": "5d87b391095c6f3e9d9461553c28b61c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "726b5f200d35035ba6c2101394e2d939", "metric_id": "b93153898b6eb6769225f710ae35a0e9", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "2cd39d266d4b01e1bcefde623b705fd1", "metric_id": "54503f70ee32d2a74612c77a3dac466e", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "ec8456de9b2a638369a3feb4f66ea690", "metric_id": "f14b099504fb58392aa4a1fdd2b910aa", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "17fdbe58066960bbbffe3588647165a7", "metric_id": "98082e839b61a34cc56638695cf42d29", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "a68c39d165a2404453ba438bb635c028", "metric_id": "f2069e606a79359b0a6cbaaa1aeee341", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "min", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last min value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 101.0, "average": 100.033333333, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "25453a6b8ae6d29e477f59192a1621db", "metric_id": "3a2591001322145cba5eb0db159931e7", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_INT", "metric_name": "min", "anomaly_score": 5.294651389, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 101.0, "min_metric_value": 99.485610776, "max_metric_value": 100.581055891, "training_avg": 100.033333333, "training_stddev": 0.1825741858, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_INT, the last min value is 101. The average for this metric is 100.033.", "is_anomalous": true}], "result_description": "In column NULL_PERCENT_INT, the last min value is 101. The average for this metric is 100.033."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_STR, the last null_count value is 175. The average for this metric is 346.067.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Null Count", "metrics": [{"value": 345.0, "average": 372.5, "min_value": 255.827381104, "max_value": 489.172618896, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "cf224643e36adb0706ebbf3857563e0b", "metric_id": "7a3a67a2e77aa0d823e82680815548fb", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_count", "anomaly_score": -0.7071067812, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 345.0, "min_metric_value": 255.827381104, "max_metric_value": 489.172618896, "training_avg": 372.5, "training_stddev": 38.890872965, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_count value is 345. The average for this metric is 372.5.", "is_anomalous": false}, {"value": 365.0, "average": 370.0, "min_value": 286.483534558, "max_value": 453.516465442, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "a0b6126f3b8a3b4b3162bb28e33d1804", "metric_id": "b54fc30b6cb78eca721d678b851eab42", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_count", "anomaly_score": -0.179605302, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 365.0, "min_metric_value": 286.483534558, "max_metric_value": 453.516465442, "training_avg": 370.0, "training_stddev": 27.838821814, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_count value is 365. The average for this metric is 370.", "is_anomalous": false}, {"value": 352.0, "average": 365.5, "min_value": 292.158333807, "max_value": 438.841666193, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "b71fb52e40032ea9eda88fabc1f7901d", "metric_id": "e8f08c14f371db419251fc8d6eb6aec5", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_count", "anomaly_score": -0.5522099797, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 352.0, "min_metric_value": 292.158333807, "max_metric_value": 438.841666193, "training_avg": 365.5, "training_stddev": 24.447222064, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_count value is 352. The average for this metric is 365.5.", "is_anomalous": false}, {"value": 379.0, "average": 368.2, "min_value": 302.152289972, "max_value": 434.247710028, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "c1d08c9cc41fbe55b8ee31fb0faaa385", "metric_id": "b8b5a94d32c401f45ef364ae1e71fefc", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_count", "anomaly_score": 0.4905544793, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 379.0, "min_metric_value": 302.152289972, "max_metric_value": 434.247710028, "training_avg": 368.2, "training_stddev": 22.015903343, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_count value is 379. The average for this metric is 368.2.", "is_anomalous": false}, {"value": 376.0, "average": 369.5, "min_value": 309.657707263, "max_value": 429.342292737, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "d122190e50bb3b94041a85b83dd43fea", "metric_id": "c74c55d5afb3297f946aa9ea740c47ec", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_count", "anomaly_score": 0.3258564989, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 376.0, "min_metric_value": 309.657707263, "max_metric_value": 429.342292737, "training_avg": 369.5, "training_stddev": 19.947430912, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_count value is 376. The average for this metric is 369.5.", "is_anomalous": false}, {"value": 349.0, "average": 366.571428571, "min_value": 307.203327468, "max_value": 425.939529674, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "d964f6ecaff8aafb46f93ad21d42e72a", "metric_id": "1eb279b367c51b955045dd9aeb0431ee", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_count", "anomaly_score": -0.8879227183, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 349.0, "min_metric_value": 307.203327468, "max_metric_value": 425.939529674, "training_avg": 366.571428571, "training_stddev": 19.789367034, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_count value is 349. The average for this metric is 366.571.", "is_anomalous": false}, {"value": 383.0, "average": 368.625, "min_value": 310.964819385, "max_value": 426.285180615, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "8a6ef15ca4049592918183bd993224a6", "metric_id": "26ffb929faf2956b7b94832a84fe74d5", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_count", "anomaly_score": 0.7479164918, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 383.0, "min_metric_value": 310.964819385, "max_metric_value": 426.285180615, "training_avg": 368.625, "training_stddev": 19.220060205, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_count value is 383. The average for this metric is 368.625.", "is_anomalous": false}, {"value": 340.0, "average": 365.444444444, "min_value": 304.382999981, "max_value": 426.505888908, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "e842a4c893bcb19f30d0a256890a4a54", "metric_id": "51e1a6f68bb7744ff6bcf88440b20366", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_count", "anomaly_score": -1.250106905, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 340.0, "min_metric_value": 304.382999981, "max_metric_value": 426.505888908, "training_avg": 365.444444444, "training_stddev": 20.353814821, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_count value is 340. The average for this metric is 365.444.", "is_anomalous": false}, {"value": 339.0, "average": 362.8, "min_value": 300.001910857, "max_value": 425.598089143, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "72a389c56f6356cfec27dfd9abfb8efb", "metric_id": "4727ae50a105eb4701b2100e4f5044f2", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_count", "anomaly_score": -1.136977271, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 339.0, "min_metric_value": 300.001910857, "max_metric_value": 425.598089143, "training_avg": 362.8, "training_stddev": 20.932696381, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_count value is 339. The average for this metric is 362.8.", "is_anomalous": false}, {"value": 370.0, "average": 363.454545455, "min_value": 303.524131261, "max_value": 423.384959648, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "c5c7edea4343078eca53cbc0e214d5ba", "metric_id": "aafd6fefe0924b67d5d4597adab97ba7", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_count", "anomaly_score": 0.3276527269, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 370.0, "min_metric_value": 303.524131261, "max_metric_value": 423.384959648, "training_avg": 363.454545455, "training_stddev": 19.976804731, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_count value is 370. The average for this metric is 363.455.", "is_anomalous": false}, {"value": 367.0, "average": 363.75, "min_value": 306.526157226, "max_value": 420.973842774, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "1f2b698771e6f379855014d3d4d9d359", "metric_id": "a05e9d5a0a91795c47bf765729e3bafc", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_count", "anomaly_score": 0.1703835242, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 367.0, "min_metric_value": 306.526157226, "max_metric_value": 420.973842774, "training_avg": 363.75, "training_stddev": 19.074614258, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_count value is 367. The average for this metric is 363.75.", "is_anomalous": false}, {"value": 370.0, "average": 364.230769231, "min_value": 309.196863598, "max_value": 419.264674864, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "47234d3d250639369a98864d28c98ab6", "metric_id": "7cedc2c76bf50ce17835a3b6a43552b1", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_count", "anomaly_score": 0.3144914414, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 370.0, "min_metric_value": 309.196863598, "max_metric_value": 419.264674864, "training_avg": 364.230769231, "training_stddev": 18.344635211, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_count value is 370. The average for this metric is 364.231.", "is_anomalous": false}, {"value": 389.0, "average": 366.0, "min_value": 309.51855308, "max_value": 422.48144692, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "33a58067150b5589043a5540b4dc536b", "metric_id": "186d4030fa9643fa64eb39d019a2e019", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_count", "anomaly_score": 1.221640092, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 389.0, "min_metric_value": 309.51855308, "max_metric_value": 422.48144692, "training_avg": 366.0, "training_stddev": 18.827148973, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_count value is 389. The average for this metric is 366.", "is_anomalous": false}, {"value": 357.0, "average": 365.4, "min_value": 310.52846171, "max_value": 420.27153829, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "5199fc5a7eb4b7ffc484e8f39246372b", "metric_id": "1a77c70cd3f86d872af2d0836b8afd23", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_count", "anomaly_score": -0.4592544839, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 357.0, "min_metric_value": 310.52846171, "max_metric_value": 420.27153829, "training_avg": 365.4, "training_stddev": 18.290512763, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_count value is 357. The average for this metric is 365.4.", "is_anomalous": false}, {"value": 352.0, "average": 364.5625, "min_value": 310.607310259, "max_value": 418.517689741, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "81d5065ae5cde621ad438e530d827146", "metric_id": "5a0327a6c6d8a8586877c3cccc0d61b1", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_count", "anomaly_score": -0.6984962926, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 352.0, "min_metric_value": 310.607310259, "max_metric_value": 418.517689741, "training_avg": 364.5625, "training_stddev": 17.985063247, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_count value is 352. The average for this metric is 364.563.", "is_anomalous": false}, {"value": 364.0, "average": 364.529411765, "min_value": 312.285920754, "max_value": 416.772902775, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "27c33e314f697caa7fe2dcd3c53ae057", "metric_id": "380709e9614ab95bd2ed7246b3bdb646", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_count", "anomaly_score": -0.03040063486, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 364.0, "min_metric_value": 312.285920754, "max_metric_value": 416.772902775, "training_avg": 364.529411765, "training_stddev": 17.414497003, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_count value is 364. The average for this metric is 364.529.", "is_anomalous": false}, {"value": 372.0, "average": 364.944444444, "min_value": 313.986272439, "max_value": 415.90261645, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "6ef5a9655b942f7fbcd8bcbf77b635a8", "metric_id": "7e4b5ce32cc97f7dbacebee58d41459c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_count", "anomaly_score": 0.415373351, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 372.0, "min_metric_value": 313.986272439, "max_metric_value": 415.90261645, "training_avg": 364.944444444, "training_stddev": 16.986057335, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_count value is 372. The average for this metric is 364.944.", "is_anomalous": false}, {"value": 352.0, "average": 364.263157895, "min_value": 313.945744356, "max_value": 414.580571434, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "b6bf6adb8c17f31baf1f5b8cd6a4462d", "metric_id": "9da3b1cb9fb2caaf0a1283fdc235f0c9", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_count", "anomaly_score": -0.7311479485, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 352.0, "min_metric_value": 313.945744356, "max_metric_value": 414.580571434, "training_avg": 364.263157895, "training_stddev": 16.77247118, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_count value is 352. The average for this metric is 364.263.", "is_anomalous": false}, {"value": 355.0, "average": 363.8, "min_value": 314.431994166, "max_value": 413.168005834, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "577fca2402a2b9e8136b37de928f96f0", "metric_id": "7aba99f4d76d35be52a9ae0f239ce1bf", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_count", "anomaly_score": -0.5347592951, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 355.0, "min_metric_value": 314.431994166, "max_metric_value": 413.168005834, "training_avg": 363.8, "training_stddev": 16.456001945, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_count value is 355. The average for this metric is 363.8.", "is_anomalous": false}, {"value": 388.0, "average": 364.952380952, "min_value": 314.293437305, "max_value": 415.611324599, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "efcee72f152635df3fbbc8ee75de53e9", "metric_id": "c676882799980a0e598a6bb3f6b5c43d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_count", "anomaly_score": 1.364869699, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 388.0, "min_metric_value": 314.293437305, "max_metric_value": 415.611324599, "training_avg": 364.952380952, "training_stddev": 16.886314549, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_count value is 388. The average for this metric is 364.952.", "is_anomalous": false}, {"value": 361.0, "average": 364.772727273, "min_value": 315.27007091, "max_value": 414.275383636, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "16078b6705b23129608fb8e8119e81d7", "metric_id": "de58fbcebf2e5c8dd63bbb725253e046", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_count", "anomaly_score": -0.2286378681, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 361.0, "min_metric_value": 315.27007091, "max_metric_value": 414.275383636, "training_avg": 364.772727273, "training_stddev": 16.500885454, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_count value is 361. The average for this metric is 364.773.", "is_anomalous": false}, {"value": 361.0, "average": 364.608695652, "min_value": 316.186638156, "max_value": 413.030753148, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "3da32de1f17b19c64fdb20cfd7c8a16e", "metric_id": "afe600f726ba6f5b3ebd8424fc4da214", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_count", "anomaly_score": -0.2235775908, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 361.0, "min_metric_value": 316.186638156, "max_metric_value": 413.030753148, "training_avg": 364.608695652, "training_stddev": 16.140685832, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_count value is 361. The average for this metric is 364.609.", "is_anomalous": false}, {"value": 364.0, "average": 364.583333333, "min_value": 317.224159942, "max_value": 411.942506725, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "b3303ccd359f1a3291129ff582926c2d", "metric_id": "32a7140500794a11e06a9242cb8a2ecf", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_count", "anomaly_score": -0.03695165846, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 364.0, "min_metric_value": 317.224159942, "max_metric_value": 411.942506725, "training_avg": 364.583333333, "training_stddev": 15.786391131, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_count value is 364. The average for this metric is 364.583.", "is_anomalous": false}, {"value": 357.0, "average": 364.28, "min_value": 317.695238543, "max_value": 410.864761457, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "b690b6529f8c74d393bf0c7da0e727c1", "metric_id": "08727e27787d85aecd97d3e3d3d63c0f", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_count", "anomaly_score": -0.4688228364, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 357.0, "min_metric_value": 317.695238543, "max_metric_value": 410.864761457, "training_avg": 364.28, "training_stddev": 15.528253819, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_count value is 357. The average for this metric is 364.28.", "is_anomalous": false}, {"value": 345.0, "average": 363.538461538, "min_value": 316.506492051, "max_value": 410.570431026, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "e9428643a18ec15b76440ec6987ef672", "metric_id": "c9474d6f1b77d2ad108a10165c2b4328", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_count", "anomaly_score": -1.182501716, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 345.0, "min_metric_value": 316.506492051, "max_metric_value": 410.570431026, "training_avg": 363.538461538, "training_stddev": 15.677323162, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_count value is 345. The average for this metric is 363.538.", "is_anomalous": false}, {"value": 346.0, "average": 362.888888889, "min_value": 315.671714699, "max_value": 410.106063079, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "eefea7a3ff036a30ab3a69303c058f72", "metric_id": "d230064f798b835d82a3b805a86d778b", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_count", "anomaly_score": -1.073055886, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 346.0, "min_metric_value": 315.671714699, "max_metric_value": 410.106063079, "training_avg": 362.888888889, "training_stddev": 15.739058063, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_count value is 346. The average for this metric is 362.889.", "is_anomalous": false}, {"value": 346.0, "average": 362.285714286, "min_value": 314.972170468, "max_value": 409.599258103, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "05bc29d1230c89dab0b0a905e2553e8c", "metric_id": "0dbb0303872f8e1376efc0af7387e4c5", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_count", "anomaly_score": -1.032624887, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 346.0, "min_metric_value": 314.972170468, "max_metric_value": 409.599258103, "training_avg": 362.285714286, "training_stddev": 15.771181273, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_count value is 346. The average for this metric is 362.286.", "is_anomalous": false}, {"value": 63.0, "average": 351.965517241, "min_value": 314.972170468, "max_value": 409.599258103, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "112358a0bd3189f475b428bb7fe9bcb0", "metric_id": "29b5060b040433e5b8b300576c6bb5c9", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_count", "anomaly_score": -5.008636228, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 63.0, "min_metric_value": 178.885159185, "max_metric_value": 525.045875297, "training_avg": 351.965517241, "training_stddev": 57.693452685, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_count value is 63. The average for this metric is 351.966.", "is_anomalous": true}, {"value": 175.0, "average": 346.066666667, "min_value": 150.314641971, "max_value": 541.818691362, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "dcc793fceb1cd141a4b025897b741ba0", "metric_id": "ddb7d12795bfb4afeea25db48240f369", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_PERCENT_STR", "metric_name": "null_count", "anomaly_score": -2.62168425, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 175.0, "min_metric_value": 150.314641971, "max_metric_value": 541.818691362, "training_avg": 346.066666667, "training_stddev": 65.250674899, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_PERCENT_STR, the last null_count value is 175. The average for this metric is 346.067.", "is_anomalous": false}], "result_description": "In column NULL_PERCENT_STR, the last null_count value is 175. The average for this metric is 346.067."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "max_length", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('max_length' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_STR, the last max_length value is 5. The average for this metric is 5.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('max_length' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Max Length", "metrics": [{"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "f4d734e46a03084219b687beca5394d0", "metric_id": "3b67766c691003043423c111448dab56", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "90443e105e42c8785d56b1a33f3da4b1", "metric_id": "9433d36af8d681e95fff461fd3cba911", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "64bd36fc67a94f722222f8b13bb626a3", "metric_id": "92dc605773505a22ab9deb8c35d875b8", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "4ad1cc3074fb0001bb56ab47968e76d1", "metric_id": "bf889f39f8f68c4d77a63ebd992fe8a0", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "337c16594650991ec404d3d2cdaceb08", "metric_id": "2da22107dd1a8a45ff092932a123ce4c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "88acda9d8f651f1865b077e874188f72", "metric_id": "0b682c4c2ec364180838092c150ccce6", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "5109b1246135e5db3a4bb092e5e85f47", "metric_id": "b84b0b5bd3e895648b0e71dfcab7116d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "2bf9146505ebea8ccdc50c08e1c764fe", "metric_id": "0547206a6d9ba509d97239e845d4fdfa", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "a58fc07a8e9cc373a48172488f686122", "metric_id": "7acf4a3854f9851a16c3d579d4784884", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "1cd69e49a7ddcd12918e3312a2a971cb", "metric_id": "bd3dbe863ae6b70944b4feebebccee5d", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "23cb4d763054461177af097fe22b1382", "metric_id": "ad4541d2be81eddc4acb707551588fd0", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "574d724a45fbc7a8ff515a00955a5746", "metric_id": "59ecc52b3589102665665b4bf6173241", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "d3196929a4ca3977901d16fc9d9f18db", "metric_id": "bee6eb72f459f7e053de16a9534e9ff7", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "49315b5fd0166b6b7e373d92d77be520", "metric_id": "1c1188415642941acb0f58d10c98cb03", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "0acc18b5119ecc5a5123f59a43949992", "metric_id": "fa7cb5de82410a44f96e2681c46a9202", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "9ed04ca9bf0c4c5453dfd0f2c7e35949", "metric_id": "0b5dfb90d78a01637a5eabfde7006eeb", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "1373e83e93076bccc48151f34d3ddd25", "metric_id": "5fcc4429c3fc8aa17634a1e4e9e3bf28", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "1d9639ddb6795196ff301b28e50aeec2", "metric_id": "d480a73e0bf79e406e189a27811ed8ed", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "84cff09e2afa1ed29e312ad21058f9e0", "metric_id": "61cf5d5f48b9cc3ae1833f61a31d0d06", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "9692f0c67a39340ddac6f0109b298bc6", "metric_id": "8d11c4981008e879d897a78561182681", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "6319af406cbb3d4c37b3c260165a04a8", "metric_id": "f1cf2dbe371434c080790f5c20922602", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "f34d77a48003ceffc927b0bf2deac820", "metric_id": "0f318dc2162d74e4409aa1205dd03767", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "03473dd9dbd006101300f1cb9e5b0625", "metric_id": "e17917d6327995d0cf12c79fed1421c9", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "32fb5877bd564695b19f81562f825b6c", "metric_id": "dcbf5abbd5e5d76f8db0988e7ef86653", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "a49fe2235faa56b6f9ca2596c90110f0", "metric_id": "8ae6feddf686ad2bd6b88887b861a4ba", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "6a2f868e082ef7834c0a3ff1bfbdf4ad", "metric_id": "3e392616d70249931709b892f69f4b1c", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "bf347ad2d9e091449b3835d6df0a745a", "metric_id": "d8520c7e59fe8d9be7cb195b4b36e20e", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "f1bfae39f748ca0ac18826b6157c213a", "metric_id": "6f39de0d0de31e6486dafd146ddbd6e2", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "7c464cfd52d1a1651ee22df126a033c0", "metric_id": "d8258f5d4d9da68921e557a1731c2932", "test_execution_id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "detected_at": "2023-01-02T10:45:27.721000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES", "column_name": "NULL_COUNT_STR", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column NULL_COUNT_STR, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}], "result_description": "In column NULL_COUNT_STR, the last max_length value is 5. The average for this metric is 5."}}], "model.elementary_integration_tests.stats_players": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_schema_changes_stats_players_.388c33d77b", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "stats_players", "column_name": null, "test_name": "schema_changes", "test_display_name": "Schema Changes", "latest_run_time": "2023-01-02T12:43:14+02:00", "latest_run_time_utc": "2023-01-02T10:43:14+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.stats_players", "table_unique_id": "elementary_tests.elon_test.stats_players", "test_type": "schema_change", "test_sub_type": "generic", "test_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.elementary_test_results\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n \n\n \n \n \n\n \n \n \n\n \n \n \n\n \n\n \n \n \n \n select * from ELEMENTARY_TESTS.elon_test_elementary.elementary_schema_changes_stats_players___schema_changes_alerts", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.elementary_test_results\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n \n\n \n \n \n\n \n \n \n\n \n \n \n\n \n\n \n \n \n \n select * from ELEMENTARY_TESTS.elon_test_elementary.elementary_schema_changes_stats_players___schema_changes_alerts"}, "configuration": {"test_name": "schema_changes", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_schema_changes_stats_players_.388c33d77b", "database_name": "ELEMENTARY_TESTS", "schema_name": "ELON_TEST", "table_name": "STATS_PLAYERS", "column_name": "KEY_CROSSES", "test_name": "schema_changes", "test_display_name": "Schema Changes", "latest_run_time": "2023-01-02T12:46:13+02:00", "latest_run_time_utc": "2023-01-02T10:46:13+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.stats_players", "table_unique_id": "elementary_tests.elon_test.stats_players", "test_type": "schema_change", "test_sub_type": "column_added", "test_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.elementary_test_results\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n \n\n \n \n \n\n \n \n \n\n \n \n \n\n \n\n \n \n \n \n select * from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_SCHEMA_CHANGES_STATS_PLAYERS___SCHEMA_CHANGES_ALERTS\"", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "The column \"KEY_CROSSES\" was added", "result_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.elementary_test_results\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n \n\n \n \n \n\n \n \n \n\n \n \n \n\n \n\n \n \n \n \n select * from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_SCHEMA_CHANGES_STATS_PLAYERS___SCHEMA_CHANGES_ALERTS\""}, "configuration": {"test_name": "schema_changes", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "column added", "metrics": null, "result_description": "The column \"KEY_CROSSES\" was added"}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_schema_changes_from_baseline_stats_players_.02157887f9", "database_name": "ELEMENTARY_TESTS", "schema_name": "ELON_TEST", "table_name": "STATS_PLAYERS", "column_name": "coffee_cups_consumed", "test_name": "schema_changes_from_baseline", "test_display_name": "Schema Changes From Baseline", "latest_run_time": "2023-01-02T12:43:23+02:00", "latest_run_time_utc": "2023-01-02T10:43:23+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.stats_players", "table_unique_id": "elementary_tests.elon_test.stats_players", "test_type": "schema_change", "test_sub_type": "column_removed", "test_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n\n \n \n \n\n \n \n \n \n\n \n\n \n\n with cur as (\n \n with baseline as (\n select lower(column_name) as column_name, data_type\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_schema_changes_from_baseline_stats_players___schema_baseline__dbt_tmp\n )\n\n select\n info_schema.full_table_name,\n lower(info_schema.column_name) as column_name,\n info_schema.data_type,\n (baseline.column_name IS NULL) as is_new,\n \n cast ('2023-01-02 10:43:23' as TIMESTAMP)\n as detected_at\n from ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns info_schema\n left join baseline on (\n lower(info_schema.column_name) = lower(baseline.column_name)\n )\n where lower(info_schema.full_table_name) = lower('ELEMENTARY_TESTS.ELON_TEST.STATS_PLAYERS')\n \n ),\n\n pre as (\n \n select\n cast('ELEMENTARY_TESTS.ELON_TEST.STATS_PLAYERS' as varchar) as full_table_name,\n column_name,\n data_type,\n \n cast ('2023-01-02 10:43:23' as TIMESTAMP)\n as detected_at\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_schema_changes_from_baseline_stats_players___schema_baseline__dbt_tmp\n \n ),\n\n type_changes as (\n\n \n select\n cur.full_table_name,\n 'type_changed' as change,\n cur.column_name,\n cur.data_type as data_type,\n pre.data_type as pre_data_type,\n pre.detected_at\n from cur inner join pre\n on (cur.full_table_name = pre.full_table_name and cur.column_name = pre.column_name)\n where pre.data_type IS NOT NULL AND cur.data_type != pre.data_type\n\n ),\n\n \n\n columns_removed as (\n\n \n select\n pre.full_table_name,\n 'column_removed' as change,\n pre.column_name as column_name,\n \n cast(null as varchar)\n as data_type,\n pre.data_type as pre_data_type,\n pre.detected_at as detected_at\n from pre left join cur\n on (cur.full_table_name = pre.full_table_name and cur.column_name = pre.column_name)\n where cur.full_table_name is null and cur.column_name is null\n\n ),\n\n columns_removed_filter_deleted_tables as (\n\n \n select\n removed.full_table_name,\n removed.change,\n removed.column_name,\n removed.data_type,\n removed.pre_data_type,\n removed.detected_at\n from columns_removed as removed join cur\n on (removed.full_table_name = cur.full_table_name)\n\n ),\n\n all_column_changes as (\n\n \n select * from type_changes\n union all\n select * from columns_removed_filter_deleted_tables\n \n ),\n\n column_changes_test_results as (\n\n \n select\n \n \n\n md5(cast(coalesce(cast(full_table_name as varchar), '') || '-' || coalesce(cast(column_name as varchar), '') || '-' || coalesce(cast(change as varchar), '') || '-' || coalesce(cast(detected_at as varchar), '') as TEXT))\n as data_issue_id,\n \n cast ('2023-01-02 10:43:23' as TIMESTAMP)\n as detected_at,\n \n trim(split(full_table_name,'.')[0],'\"') as database_name\n\n,\n \n trim(split(full_table_name,'.')[1],'\"') as schema_name\n\n,\n \n trim(split(full_table_name,'.')[2],'\"') as table_name\n\n,\n column_name,\n 'schema_change' as test_type,\n change as test_sub_type,\n case\n when change = 'column_added'\n then 'The column \"' || column_name || '\" was added'\n when change= 'column_removed'\n then 'The column \"' || column_name || '\" was removed'\n when change= 'type_changed'\n then 'The type of \"' || column_name || '\" was changed from ' || pre_data_type || ' to ' || data_type\n else NULL\n end as test_results_description\n from all_column_changes\n group by 1,2,3,4,5,6,7,8,9\n\n )\n\n \n select \n \n\n md5(cast(coalesce(cast(data_issue_id as varchar), '') || '-' || coalesce(cast(cast('acd0caa6-5efb-4df9-b512-b449e218426f.test.elementary_integration_tests.elementary_schema_changes_from_baseline_stats_players_.02157887f9' as varchar) as varchar), '') as TEXT))\n as id,\n cast('acd0caa6-5efb-4df9-b512-b449e218426f.test.elementary_integration_tests.elementary_schema_changes_from_baseline_stats_players_.02157887f9' as varchar) as test_execution_id,\n cast('test.elementary_integration_tests.elementary_schema_changes_from_baseline_stats_players_.02157887f9' as varchar) as test_unique_id,\n *\n from column_changes_test_results", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "The column \"coffee_cups_consumed\" was removed", "result_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n\n \n \n \n\n \n \n \n \n\n \n\n \n\n with cur as (\n \n with baseline as (\n select lower(column_name) as column_name, data_type\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_schema_changes_from_baseline_stats_players___schema_baseline__dbt_tmp\n )\n\n select\n info_schema.full_table_name,\n lower(info_schema.column_name) as column_name,\n info_schema.data_type,\n (baseline.column_name IS NULL) as is_new,\n \n cast ('2023-01-02 10:43:23' as TIMESTAMP)\n as detected_at\n from ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns info_schema\n left join baseline on (\n lower(info_schema.column_name) = lower(baseline.column_name)\n )\n where lower(info_schema.full_table_name) = lower('ELEMENTARY_TESTS.ELON_TEST.STATS_PLAYERS')\n \n ),\n\n pre as (\n \n select\n cast('ELEMENTARY_TESTS.ELON_TEST.STATS_PLAYERS' as varchar) as full_table_name,\n column_name,\n data_type,\n \n cast ('2023-01-02 10:43:23' as TIMESTAMP)\n as detected_at\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_schema_changes_from_baseline_stats_players___schema_baseline__dbt_tmp\n \n ),\n\n type_changes as (\n\n \n select\n cur.full_table_name,\n 'type_changed' as change,\n cur.column_name,\n cur.data_type as data_type,\n pre.data_type as pre_data_type,\n pre.detected_at\n from cur inner join pre\n on (cur.full_table_name = pre.full_table_name and cur.column_name = pre.column_name)\n where pre.data_type IS NOT NULL AND cur.data_type != pre.data_type\n\n ),\n\n \n\n columns_removed as (\n\n \n select\n pre.full_table_name,\n 'column_removed' as change,\n pre.column_name as column_name,\n \n cast(null as varchar)\n as data_type,\n pre.data_type as pre_data_type,\n pre.detected_at as detected_at\n from pre left join cur\n on (cur.full_table_name = pre.full_table_name and cur.column_name = pre.column_name)\n where cur.full_table_name is null and cur.column_name is null\n\n ),\n\n columns_removed_filter_deleted_tables as (\n\n \n select\n removed.full_table_name,\n removed.change,\n removed.column_name,\n removed.data_type,\n removed.pre_data_type,\n removed.detected_at\n from columns_removed as removed join cur\n on (removed.full_table_name = cur.full_table_name)\n\n ),\n\n all_column_changes as (\n\n \n select * from type_changes\n union all\n select * from columns_removed_filter_deleted_tables\n \n ),\n\n column_changes_test_results as (\n\n \n select\n \n \n\n md5(cast(coalesce(cast(full_table_name as varchar), '') || '-' || coalesce(cast(column_name as varchar), '') || '-' || coalesce(cast(change as varchar), '') || '-' || coalesce(cast(detected_at as varchar), '') as TEXT))\n as data_issue_id,\n \n cast ('2023-01-02 10:43:23' as TIMESTAMP)\n as detected_at,\n \n trim(split(full_table_name,'.')[0],'\"') as database_name\n\n,\n \n trim(split(full_table_name,'.')[1],'\"') as schema_name\n\n,\n \n trim(split(full_table_name,'.')[2],'\"') as table_name\n\n,\n column_name,\n 'schema_change' as test_type,\n change as test_sub_type,\n case\n when change = 'column_added'\n then 'The column \"' || column_name || '\" was added'\n when change= 'column_removed'\n then 'The column \"' || column_name || '\" was removed'\n when change= 'type_changed'\n then 'The type of \"' || column_name || '\" was changed from ' || pre_data_type || ' to ' || data_type\n else NULL\n end as test_results_description\n from all_column_changes\n group by 1,2,3,4,5,6,7,8,9\n\n )\n\n \n select \n \n\n md5(cast(coalesce(cast(data_issue_id as varchar), '') || '-' || coalesce(cast(cast('acd0caa6-5efb-4df9-b512-b449e218426f.test.elementary_integration_tests.elementary_schema_changes_from_baseline_stats_players_.02157887f9' as varchar) as varchar), '') as TEXT))\n as id,\n cast('acd0caa6-5efb-4df9-b512-b449e218426f.test.elementary_integration_tests.elementary_schema_changes_from_baseline_stats_players_.02157887f9' as varchar) as test_execution_id,\n cast('test.elementary_integration_tests.elementary_schema_changes_from_baseline_stats_players_.02157887f9' as varchar) as test_unique_id,\n *\n from column_changes_test_results"}, "configuration": {"test_name": "schema_changes_from_baseline", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "column removed", "metrics": null, "result_description": "The column \"coffee_cups_consumed\" was removed"}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_schema_changes_from_baseline_stats_players_.02157887f9", "database_name": "ELEMENTARY_TESTS", "schema_name": "ELON_TEST", "table_name": "STATS_PLAYERS", "column_name": "goals", "test_name": "schema_changes_from_baseline", "test_display_name": "Schema Changes From Baseline", "latest_run_time": "2023-01-02T12:43:23+02:00", "latest_run_time_utc": "2023-01-02T10:43:23+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.stats_players", "table_unique_id": "elementary_tests.elon_test.stats_players", "test_type": "schema_change", "test_sub_type": "type_changed", "test_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n\n \n \n \n\n \n \n \n \n\n \n\n \n\n with cur as (\n \n with baseline as (\n select lower(column_name) as column_name, data_type\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_schema_changes_from_baseline_stats_players___schema_baseline__dbt_tmp\n )\n\n select\n info_schema.full_table_name,\n lower(info_schema.column_name) as column_name,\n info_schema.data_type,\n (baseline.column_name IS NULL) as is_new,\n \n cast ('2023-01-02 10:43:23' as TIMESTAMP)\n as detected_at\n from ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns info_schema\n left join baseline on (\n lower(info_schema.column_name) = lower(baseline.column_name)\n )\n where lower(info_schema.full_table_name) = lower('ELEMENTARY_TESTS.ELON_TEST.STATS_PLAYERS')\n \n ),\n\n pre as (\n \n select\n cast('ELEMENTARY_TESTS.ELON_TEST.STATS_PLAYERS' as varchar) as full_table_name,\n column_name,\n data_type,\n \n cast ('2023-01-02 10:43:23' as TIMESTAMP)\n as detected_at\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_schema_changes_from_baseline_stats_players___schema_baseline__dbt_tmp\n \n ),\n\n type_changes as (\n\n \n select\n cur.full_table_name,\n 'type_changed' as change,\n cur.column_name,\n cur.data_type as data_type,\n pre.data_type as pre_data_type,\n pre.detected_at\n from cur inner join pre\n on (cur.full_table_name = pre.full_table_name and cur.column_name = pre.column_name)\n where pre.data_type IS NOT NULL AND cur.data_type != pre.data_type\n\n ),\n\n \n\n columns_removed as (\n\n \n select\n pre.full_table_name,\n 'column_removed' as change,\n pre.column_name as column_name,\n \n cast(null as varchar)\n as data_type,\n pre.data_type as pre_data_type,\n pre.detected_at as detected_at\n from pre left join cur\n on (cur.full_table_name = pre.full_table_name and cur.column_name = pre.column_name)\n where cur.full_table_name is null and cur.column_name is null\n\n ),\n\n columns_removed_filter_deleted_tables as (\n\n \n select\n removed.full_table_name,\n removed.change,\n removed.column_name,\n removed.data_type,\n removed.pre_data_type,\n removed.detected_at\n from columns_removed as removed join cur\n on (removed.full_table_name = cur.full_table_name)\n\n ),\n\n all_column_changes as (\n\n \n select * from type_changes\n union all\n select * from columns_removed_filter_deleted_tables\n \n ),\n\n column_changes_test_results as (\n\n \n select\n \n \n\n md5(cast(coalesce(cast(full_table_name as varchar), '') || '-' || coalesce(cast(column_name as varchar), '') || '-' || coalesce(cast(change as varchar), '') || '-' || coalesce(cast(detected_at as varchar), '') as TEXT))\n as data_issue_id,\n \n cast ('2023-01-02 10:43:23' as TIMESTAMP)\n as detected_at,\n \n trim(split(full_table_name,'.')[0],'\"') as database_name\n\n,\n \n trim(split(full_table_name,'.')[1],'\"') as schema_name\n\n,\n \n trim(split(full_table_name,'.')[2],'\"') as table_name\n\n,\n column_name,\n 'schema_change' as test_type,\n change as test_sub_type,\n case\n when change = 'column_added'\n then 'The column \"' || column_name || '\" was added'\n when change= 'column_removed'\n then 'The column \"' || column_name || '\" was removed'\n when change= 'type_changed'\n then 'The type of \"' || column_name || '\" was changed from ' || pre_data_type || ' to ' || data_type\n else NULL\n end as test_results_description\n from all_column_changes\n group by 1,2,3,4,5,6,7,8,9\n\n )\n\n \n select \n \n\n md5(cast(coalesce(cast(data_issue_id as varchar), '') || '-' || coalesce(cast(cast('acd0caa6-5efb-4df9-b512-b449e218426f.test.elementary_integration_tests.elementary_schema_changes_from_baseline_stats_players_.02157887f9' as varchar) as varchar), '') as TEXT))\n as id,\n cast('acd0caa6-5efb-4df9-b512-b449e218426f.test.elementary_integration_tests.elementary_schema_changes_from_baseline_stats_players_.02157887f9' as varchar) as test_execution_id,\n cast('test.elementary_integration_tests.elementary_schema_changes_from_baseline_stats_players_.02157887f9' as varchar) as test_unique_id,\n *\n from column_changes_test_results", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "The type of \"goals\" was changed from BOOLEAN to NUMBER", "result_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n\n \n \n \n\n \n \n \n \n\n \n\n \n\n with cur as (\n \n with baseline as (\n select lower(column_name) as column_name, data_type\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_schema_changes_from_baseline_stats_players___schema_baseline__dbt_tmp\n )\n\n select\n info_schema.full_table_name,\n lower(info_schema.column_name) as column_name,\n info_schema.data_type,\n (baseline.column_name IS NULL) as is_new,\n \n cast ('2023-01-02 10:43:23' as TIMESTAMP)\n as detected_at\n from ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns info_schema\n left join baseline on (\n lower(info_schema.column_name) = lower(baseline.column_name)\n )\n where lower(info_schema.full_table_name) = lower('ELEMENTARY_TESTS.ELON_TEST.STATS_PLAYERS')\n \n ),\n\n pre as (\n \n select\n cast('ELEMENTARY_TESTS.ELON_TEST.STATS_PLAYERS' as varchar) as full_table_name,\n column_name,\n data_type,\n \n cast ('2023-01-02 10:43:23' as TIMESTAMP)\n as detected_at\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_schema_changes_from_baseline_stats_players___schema_baseline__dbt_tmp\n \n ),\n\n type_changes as (\n\n \n select\n cur.full_table_name,\n 'type_changed' as change,\n cur.column_name,\n cur.data_type as data_type,\n pre.data_type as pre_data_type,\n pre.detected_at\n from cur inner join pre\n on (cur.full_table_name = pre.full_table_name and cur.column_name = pre.column_name)\n where pre.data_type IS NOT NULL AND cur.data_type != pre.data_type\n\n ),\n\n \n\n columns_removed as (\n\n \n select\n pre.full_table_name,\n 'column_removed' as change,\n pre.column_name as column_name,\n \n cast(null as varchar)\n as data_type,\n pre.data_type as pre_data_type,\n pre.detected_at as detected_at\n from pre left join cur\n on (cur.full_table_name = pre.full_table_name and cur.column_name = pre.column_name)\n where cur.full_table_name is null and cur.column_name is null\n\n ),\n\n columns_removed_filter_deleted_tables as (\n\n \n select\n removed.full_table_name,\n removed.change,\n removed.column_name,\n removed.data_type,\n removed.pre_data_type,\n removed.detected_at\n from columns_removed as removed join cur\n on (removed.full_table_name = cur.full_table_name)\n\n ),\n\n all_column_changes as (\n\n \n select * from type_changes\n union all\n select * from columns_removed_filter_deleted_tables\n \n ),\n\n column_changes_test_results as (\n\n \n select\n \n \n\n md5(cast(coalesce(cast(full_table_name as varchar), '') || '-' || coalesce(cast(column_name as varchar), '') || '-' || coalesce(cast(change as varchar), '') || '-' || coalesce(cast(detected_at as varchar), '') as TEXT))\n as data_issue_id,\n \n cast ('2023-01-02 10:43:23' as TIMESTAMP)\n as detected_at,\n \n trim(split(full_table_name,'.')[0],'\"') as database_name\n\n,\n \n trim(split(full_table_name,'.')[1],'\"') as schema_name\n\n,\n \n trim(split(full_table_name,'.')[2],'\"') as table_name\n\n,\n column_name,\n 'schema_change' as test_type,\n change as test_sub_type,\n case\n when change = 'column_added'\n then 'The column \"' || column_name || '\" was added'\n when change= 'column_removed'\n then 'The column \"' || column_name || '\" was removed'\n when change= 'type_changed'\n then 'The type of \"' || column_name || '\" was changed from ' || pre_data_type || ' to ' || data_type\n else NULL\n end as test_results_description\n from all_column_changes\n group by 1,2,3,4,5,6,7,8,9\n\n )\n\n \n select \n \n\n md5(cast(coalesce(cast(data_issue_id as varchar), '') || '-' || coalesce(cast(cast('acd0caa6-5efb-4df9-b512-b449e218426f.test.elementary_integration_tests.elementary_schema_changes_from_baseline_stats_players_.02157887f9' as varchar) as varchar), '') as TEXT))\n as id,\n cast('acd0caa6-5efb-4df9-b512-b449e218426f.test.elementary_integration_tests.elementary_schema_changes_from_baseline_stats_players_.02157887f9' as varchar) as test_execution_id,\n cast('test.elementary_integration_tests.elementary_schema_changes_from_baseline_stats_players_.02157887f9' as varchar) as test_unique_id,\n *\n from column_changes_test_results"}, "configuration": {"test_name": "schema_changes_from_baseline", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "type changed", "metrics": null, "result_description": "The type of \"goals\" was changed from BOOLEAN to NUMBER"}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_schema_changes_stats_players_.388c33d77b", "database_name": "ELEMENTARY_TESTS", "schema_name": "ELON_TEST", "table_name": "STATS_PLAYERS", "column_name": "OFFSIDES", "test_name": "schema_changes", "test_display_name": "Schema Changes", "latest_run_time": "2023-01-02T12:46:13+02:00", "latest_run_time_utc": "2023-01-02T10:46:13+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.stats_players", "table_unique_id": "elementary_tests.elon_test.stats_players", "test_type": "schema_change", "test_sub_type": "column_removed", "test_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.elementary_test_results\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n \n\n \n \n \n\n \n \n \n\n \n \n \n\n \n\n \n \n \n \n select * from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_SCHEMA_CHANGES_STATS_PLAYERS___SCHEMA_CHANGES_ALERTS\"", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "The column \"OFFSIDES\" was removed", "result_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.elementary_test_results\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n \n\n \n \n \n\n \n \n \n\n \n \n \n\n \n\n \n \n \n \n select * from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_SCHEMA_CHANGES_STATS_PLAYERS___SCHEMA_CHANGES_ALERTS\""}, "configuration": {"test_name": "schema_changes", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "column removed", "metrics": null, "result_description": "The column \"OFFSIDES\" was removed"}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_schema_changes_stats_players_.388c33d77b", "database_name": "ELEMENTARY_TESTS", "schema_name": "ELON_TEST", "table_name": "STATS_PLAYERS", "column_name": "RED_CARDS", "test_name": "schema_changes", "test_display_name": "Schema Changes", "latest_run_time": "2023-01-02T12:46:13+02:00", "latest_run_time_utc": "2023-01-02T10:46:13+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.stats_players", "table_unique_id": "elementary_tests.elon_test.stats_players", "test_type": "schema_change", "test_sub_type": "column_added", "test_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.elementary_test_results\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n \n\n \n \n \n\n \n \n \n\n \n \n \n\n \n\n \n \n \n \n select * from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_SCHEMA_CHANGES_STATS_PLAYERS___SCHEMA_CHANGES_ALERTS\"", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "The column \"RED_CARDS\" was added", "result_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.elementary_test_results\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n \n\n \n \n \n\n \n \n \n\n \n \n \n\n \n\n \n \n \n \n select * from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_SCHEMA_CHANGES_STATS_PLAYERS___SCHEMA_CHANGES_ALERTS\""}, "configuration": {"test_name": "schema_changes", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "column added", "metrics": null, "result_description": "The column \"RED_CARDS\" was added"}}], "source.elementary_integration_tests.training.numeric_column_anomalies_training": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.singular_test_with_source_ref", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies_training", "column_name": null, "test_name": "singular_test_with_source_ref", "test_display_name": "Singular Test With Source Ref", "latest_run_time": "2023-01-02T12:46:31+02:00", "latest_run_time_utc": "2023-01-02T10:46:31+00:00", "latest_run_status": "fail", "model_unique_id": "source.elementary_integration_tests.training.numeric_column_anomalies_training", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies_training", "test_type": "dbt_test", "test_sub_type": "singular", "test_query": "select min from ELEMENTARY_TESTS.test_seeds.numeric_column_anomalies_training where min < 105", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "Got 317 results, configured to fail if != 0", "result_query": "select min from ELEMENTARY_TESTS.test_seeds.numeric_column_anomalies_training where min < 105"}, "configuration": {"test_name": "singular_test_with_source_ref", "test_params": null}}, "test_results": {"display_name": "singular_test_with_source_ref", "results_sample": [{"min": 104.0}, {"min": 103.0}, {"min": 100.0}, {"min": 100.0}, {"min": 100.0}], "error_message": "Got 317 results, configured to fail if != 0", "failed_rows_count": 317}}], "source.elementary_integration_tests.training.any_type_column_anomalies_training": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_any_type_column_anomalies_training_.e6ec3c50e5", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies_training", "column_name": null, "test_name": "volume_anomalies", "test_display_name": "Volume Anomalies", "latest_run_time": "2023-01-02T12:42:19+02:00", "latest_run_time_utc": "2023-01-02T10:42:19+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.training.any_type_column_anomalies_training", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies_training", "test_type": "anomaly_detection", "test_sub_type": "row_count", "test_query": "select * from (\n \n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_SOURCE_VOLUME_ANOMALIES_TRAINING_ANY_TYPE_COLUMN_ANOMALIES_TRAINING___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n\n \n\n\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_TRAINING' as varchar)) and\n metric_name = cast('row_count' as varchar)", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n \n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_SOURCE_VOLUME_ANOMALIES_TRAINING_ANY_TYPE_COLUMN_ANOMALIES_TRAINING___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n\n \n\n\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_TRAINING' as varchar)) and\n metric_name = cast('row_count' as varchar)"}, "configuration": {"test_name": "volume_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Row Count", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_any_type_column_anomalies_training_.5733415eda", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies_training", "column_name": null, "test_name": "freshness_anomalies", "test_display_name": "Freshness Anomalies", "latest_run_time": "2023-01-02T12:42:23+02:00", "latest_run_time_utc": "2023-01-02T10:42:23+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.training.any_type_column_anomalies_training", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies_training", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_SOURCE_FRESHNESS_ANOMALIES_TRAINING_ANY_TYPE_COLUMN_ANOMALIES_TRAINING___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_SOURCE_FRESHNESS_ANOMALIES_TRAINING_ANY_TYPE_COLUMN_ANOMALIES_TRAINING___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value"}, "configuration": {"test_name": "freshness_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": [], "result_description": null}}], "model.elementary_integration_tests.string_column_anomalies": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "string_column_anomalies", "column_name": "MISSING_PERCENT", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:00+02:00", "latest_run_time_utc": "2023-01-02T10:44:00+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.elon_test.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "average_length", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average_length' as varchar)\n and upper(column_name) = upper(cast('MISSING_PERCENT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column MISSING_PERCENT, the last average_length value is 5. The average for this metric is 5.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average_length' as varchar)\n and upper(column_name) = upper(cast('MISSING_PERCENT' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Average Length", "metrics": [{"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "f1774b693772234eab968108ed546691", "metric_id": "e81923089742cc82297f97355a63da91", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "6850adbe226968d961c82c7387a38e8e", "metric_id": "a7da61630dd76cd8df3a9170aa1341db", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "04f6092700e7ca8018566a12ac45091a", "metric_id": "efc1d5f3459d263d37461d4a8223b8fe", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "1c75edc1d859e9754852cb53dd77a11a", "metric_id": "b52f35bdae31344a6ece0efc1e6cd1a5", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "2162c8411f8819269857daba2905a8a6", "metric_id": "14ccfa0818563876b56992ac4ac9aa8c", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "58fb5b127e53e10ba7e13553a0b33dac", "metric_id": "7f32c2d0ba8ecbd7a1d32e4522063256", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "c49e7a18792a75e3dbb7d5f03228d231", "metric_id": "5d87acd3f2cf688c6c36c9303987aafb", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "7681157dbb43b292f731db624fab2a6f", "metric_id": "d1bdbd81d7ab9811acb73f7b02f50959", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "923e24bdcf86f4d19a0848022f876e9e", "metric_id": "a4b2eb981b64fa225024fb067caf5933", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "0954c2b4ae1e77ab81020c2dcf4fc416", "metric_id": "64610a6bc7f50391d056a9da4076b45b", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "54d14d1d553ea6b0c54eab9a0f355c45", "metric_id": "2d2608f583dd4449b0e270b56b02f5f0", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "73d2414d8a1166822134d7f95b2bbd86", "metric_id": "66705d1c79ffdc902aa78469aa9204ff", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "5f7f141d616d6b9df307a13efec243cb", "metric_id": "d3a3e0a04c891eaad9b5c918eefaea0f", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "2a47532f5b6b992db236e46852788785", "metric_id": "c324903d991d2e0a676db2731b761308", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "84bf211a99df7a125954d2ee694e6eb4", "metric_id": "91a30ea5f1cba267544069d3d5d968c7", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "e5c25adcd1bb35d20b205ded1fca61db", "metric_id": "d5daaf7fff8990095b2c215f330a8cc6", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "714b0ecf1bab299fc31b4d0fe15ebd49", "metric_id": "ee66eb9089df13ef3211a64a2692f497", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "37632a90e644ec25affc42dd380b2f7a", "metric_id": "9473e191387d29d601acde3b4a93ffe8", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "20b3c7e41f3a30d1de9d634c58090244", "metric_id": "b74453b07958f311479292f005b05d8c", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "6bbebaaf426845fc04c472bed35fb50d", "metric_id": "dcd45d9436b84c51fd5c7b835103cc7f", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "1c8d2a4093811785d6282159daaa9f50", "metric_id": "88367ccd0e0fa8f4cff9435734771305", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "feb12525c65886c87e935df46eaed9df", "metric_id": "ffa6755f1a460349286b2b8269b3de61", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "5651bbcec9f974b52acc0b841c831644", "metric_id": "af7b7a4ab606dbc1c1aca45be4274556", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "d026b53360ebb3fc745902da7a4e6248", "metric_id": "bf557a59dcf09e7ab6b85720e388ec01", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "8e1b2ac224210c3da268ad3801d4e2b9", "metric_id": "6ffad220c412bd0d55bfc785e9dea1f1", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "ac13efdc2cc4fc01437ef14d25f503da", "metric_id": "b2a15932b8bed26c13b6e3b08aa9b687", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "c40eae217570edd44f95d14a7fdb916c", "metric_id": "2cce2e5a243d5bfb06f9e4d56baacb10", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "675dfb53351435eff3028161e8b7d164", "metric_id": "6ebaf91a7b94c2bd2310b49dca433883", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "d0881e9a927f2505a6c64c0e49e6480a", "metric_id": "6a46bc0439dee3e21bbe6513ea4d5c1d", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}], "result_description": "In column MISSING_PERCENT, the last average_length value is 5. The average for this metric is 5."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "string_column_anomalies", "column_name": "MISSING_COUNT", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:00+02:00", "latest_run_time_utc": "2023-01-02T10:44:00+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.elon_test.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "missing_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_count__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('missing_percent' as varchar)\n and upper(column_name) = upper(cast('MISSING_COUNT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column MISSING_COUNT, the last missing_percent value is 20. The average for this metric is 3.567.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_count__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('missing_percent' as varchar)\n and upper(column_name) = upper(cast('MISSING_COUNT' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Missing Percent", "metrics": [{"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "360755ed4a90b508abdf959b97106891", "metric_id": "e18736fefed4e7534c6d8939a33632dc", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "ebbaaeea0e6ddc5266e776efb63a1193", "metric_id": "4966cd008bb808907aa924af2002db59", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "6f79608cc9ce9042f38e06eedb05ad8e", "metric_id": "d3fe094b0a9a049bc218c382b99aab4f", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "52a2da66ea8d9640d9dcadbf68ab5077", "metric_id": "3359b6255fc818dbb81a755a9fb58d81", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "652d7467ba20e409c5efc45fae891dc8", "metric_id": "89cd102ed9df910c5eeee47f8dcb44d7", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "a30f9cde790ce9fa298ec8edcc38436e", "metric_id": "f9451fa21fb63f25c33d0f878eacaec0", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "69127027e09e15f2d1eb263f7f930c0f", "metric_id": "f5f94a93043d6517387da53e51a63333", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "0eb7975fec14403eb26ef6b6d22cdbb9", "metric_id": "4f67697adfad87d56fd869d5659bb2bc", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "8e5e55b9ffa7987685cc052f26de4356", "metric_id": "f4344b4f506ed369e39e9641a0622cc4", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "4e02e940798b8cb6cb799e8662933573", "metric_id": "dcadf16a54b85399e7772a55329dca2f", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "e4dce52b5f6ed7e3b62570ba7e1ab80e", "metric_id": "a52b277afe6e481ebe9039e242f7fe28", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "3b5b2b3be18f3411b14950c6f73de805", "metric_id": "d40508f741c09bae31f130669baffefe", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "68e8ae67202a6931261bf8f38ef1435f", "metric_id": "bad1b17a38938c39664e927d7ed3f7fc", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "13eb0d5376aa24d14885df11fd71efa7", "metric_id": "1cb4d1b2a339ebb9f1ab344126591a49", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "65d10f1622ff78be25e2aebe0791cb4d", "metric_id": "cc022ca8168bed3a6bd09181281c5027", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "53169e6f683ab86c8fbaacbc2509e0b5", "metric_id": "830b10db9a135f01315929cceaf828da", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "da640b10d2d5fb965519375cc8544569", "metric_id": "34689eaee989dfb195b8f26b67e2a70d", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "29d5c1cf98b10a0e5c1789acbfad764e", "metric_id": "849dc25ad93d355f228d71ca85afae51", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "8da90992bdec3022e2e7ad3ff8e37a8c", "metric_id": "bbe70831d26a2dff5be3c5c8f5b323cf", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "41ebb8951a0f1d24f643485a25630b0c", "metric_id": "6f127ca19a8de7fe0ed8fc125563af2a", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "bd657097c57ecb83dd756ab54f273d31", "metric_id": "b30fd7d2aa5097c2fd3792f871d28ba1", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "ca0b45153b2cb084dad4df2732a89243", "metric_id": "a29927153a8c869415e247cfda0ac18c", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "76ac29965202e164ed19ee7f82b5addb", "metric_id": "f2c8fce89c1b8fcf5abf2b92f834fd73", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "79fa0fef833387e6de56f39db67efbce", "metric_id": "9f01c0ef2277b5f43c7ad39dc04d1939", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "6e93364d760db0d7b3d41f88196df4bc", "metric_id": "66a115510c9734102112384166d4ea71", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "fd4a29fdbd0ce56176e19ad928f15c97", "metric_id": "3d2c8c2a92b862f3bfa433a80ca36280", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "48465041d83bfe4dfa6473957cfb71d7", "metric_id": "6b1b5c9d5e32438e7d2ad9755b412105", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "52083e2ef54dc9d8259e05167b90b0a5", "metric_id": "c83be13d2d5a7a5b129116627f43cecd", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 20.0, "average": 3.566666667, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "c51ad1ce82c763f72f038c8e4c908de5", "metric_id": "ada51d80df91e0e91ac30c9392b87192", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_percent", "anomaly_score": 5.294651389, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 20.0, "min_metric_value": -5.744616811, "max_metric_value": 12.877950144, "training_avg": 3.566666667, "training_stddev": 3.103761159, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_percent value is 20. The average for this metric is 3.567.", "is_anomalous": true}], "result_description": "In column MISSING_COUNT, the last missing_percent value is 20. The average for this metric is 3.567."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "string_column_anomalies", "column_name": "MISSING_PERCENT", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:00+02:00", "latest_run_time_utc": "2023-01-02T10:44:00+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.elon_test.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "min_length", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('min_length' as varchar)\n and upper(column_name) = upper(cast('MISSING_PERCENT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column MISSING_PERCENT, the last min_length value is 5. The average for this metric is 5.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('min_length' as varchar)\n and upper(column_name) = upper(cast('MISSING_PERCENT' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Min Length", "metrics": [{"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "3a16a856995404e5deb954cf88d6d4c1", "metric_id": "a984b6f1c47c016e50ae4341c4b42c57", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "aa5ed8f48b829eb9067dc9d91040307f", "metric_id": "88152100f93f6c333197fc84fff6fe41", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "0299919f8a5f292b84124875ea5a6885", "metric_id": "f970c33bf0ba52ddf5c923b6ba9441ad", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "5b57b8cafd59043493dd7a81d411c4fc", "metric_id": "a197b70dd2797c582710d5355f7c0094", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "431ca8e8d679053ac98ac52fa8998954", "metric_id": "f5912d19ae42e0e1a85b6d8da8593d6a", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "c9669a4c10bef8e1bceb1cacb9604e68", "metric_id": "1fb343f397c681e17bae3d645f600dae", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "8405f254f5de8863a83aa93a156a125b", "metric_id": "277b0df65cd6d88df4c181f3b66efd81", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "88c2cc63ca032ec389c98fee076fab3b", "metric_id": "6bbe54894bc7e34812dc893c0f0b04d6", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "ac42fac49dcdbc15d70a39f57e71e1bd", "metric_id": "25b9bfa3e846ba47c5e4e96fe1b2440a", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "94ab79d62d234d74e55169dba271bdc9", "metric_id": "6dc3adf073b6cbc5015068109d8cd8a6", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "1798cf1c16600dea1c44b456a0c5fba7", "metric_id": "7ebae2c75343282ecbc70d162c9559a5", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "8e674830735b36f5863873f52dbc5b31", "metric_id": "bdcf6624dfd6d1bbe510117b2b71f6b9", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "06cc74130c18cfe11ca43111f4d8edf6", "metric_id": "0db485c5fe5f43dd83fc047fd631ebe2", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "16d7e13d5380df3c3bbdbaec31f1a5f3", "metric_id": "54c43fb11090ada051a47b0c14dd1852", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "a564879e020b17f53c62c42420ee706a", "metric_id": "51cbe6c3ac01f0c846cfbafd67a90b31", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "b8dcf9bff6483406aa9fe927a1b3475d", "metric_id": "c72df8027cdbd573e4092b9c7845bc67", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "8ea5d9d24e616563871d0e67bcc109e5", "metric_id": "8e235ecedac5564a820017cda5c54cbd", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "be0cb0d9f9e4866070b219aedb11524d", "metric_id": "1f609e9af1a9485f0373c9d106b4f5b4", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "fbb5de2d31c40617b05e9a7709ed621b", "metric_id": "63d3ab6c453d07905a7e9eacd0519fbf", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "1d82f80aefee85166051b7c51b2c174f", "metric_id": "d701ff34687e8a32451d3f1bd428631e", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "62428b202b9a182b943f90288c7d7e3e", "metric_id": "934103829f0a3f05e76f927460425d97", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "bf1951f90788525d3286fd06435b0ec2", "metric_id": "3d3ef28479c8b168f6ec7d463b924b8d", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "a0757bc7c7c3ee60a5e19f018bb9c650", "metric_id": "09d4f7a7c008c800563cbe52d76889d4", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "49dacbbead69d21891aaa9f3c16e3d90", "metric_id": "de107b06a4391a59cd91572cd0238558", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "97cac769a00f126ba50a3def8c0af293", "metric_id": "942b3a3544a6be89c1e52aac0f2cefdc", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "c8ade953628e5f2b242b858dff77afeb", "metric_id": "3fca6d94356d60f04b2dc92af3fcf27a", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "512fbbf7ef335dad1ed8fd63cac951b8", "metric_id": "6160532005c013f47cba6cb0d3a1c24e", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "7740c3ac36926a6a5a7791bf13597ad4", "metric_id": "891b37ace9eed6f6360f15bfef5d0da9", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "e70fea9d4fa89efeb439755ce509bc05", "metric_id": "667fe321dbbc78b12c28696a39533b35", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}], "result_description": "In column MISSING_PERCENT, the last min_length value is 5. The average for this metric is 5."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.relationships_string_column_anomalies_min_length__max_length__source_training_string_column_anomalies_training_.a2cbc353c6", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "string_column_anomalies", "column_name": "min_length", "test_name": "relationships", "test_display_name": "Relationships", "latest_run_time": "2023-01-02T12:46:31+02:00", "latest_run_time_utc": "2023-01-02T10:46:31+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.elon_test.string_column_anomalies", "test_type": "dbt_test", "test_sub_type": "generic", "test_query": "with child as (\n select min_length as from_field\n from ELEMENTARY_TESTS.elon_test.string_column_anomalies\n where min_length is not null\n),\n\nparent as (\n select max_length as to_field\n from ELEMENTARY_TESTS.test_seeds.string_column_anomalies_training\n)\n\nselect\n from_field\n\nfrom child\nleft join parent\n on child.from_field = parent.to_field\n\nwhere parent.to_field is null", "test_params": {}, "test_created_at": null, "description": "This test validates that all of the records in a child table have a corresponding record in a parent table. This property is referred to as \"referential integrity\".", "result": {"result_description": "Got 3100 results, configured to fail if != 0", "result_query": "with child as (\n select min_length as from_field\n from ELEMENTARY_TESTS.elon_test.string_column_anomalies\n where min_length is not null\n),\n\nparent as (\n select max_length as to_field\n from ELEMENTARY_TESTS.test_seeds.string_column_anomalies_training\n)\n\nselect\n from_field\n\nfrom child\nleft join parent\n on child.from_field = parent.to_field\n\nwhere parent.to_field is null"}, "configuration": {"test_name": "relationships", "test_params": null}}, "test_results": {"display_name": "relationships", "results_sample": [{"from_field": "wqjavmfvlw"}, {"from_field": "sjloxfpqr"}, {"from_field": "cywfq"}, {"from_field": "fuutf"}, {"from_field": "gxckgsdzb"}], "error_message": "Got 3100 results, configured to fail if != 0", "failed_rows_count": 3100}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "string_column_anomalies", "column_name": "MISSING_COUNT", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:00+02:00", "latest_run_time_utc": "2023-01-02T10:44:00+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.elon_test.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "max_length", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_count__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('max_length' as varchar)\n and upper(column_name) = upper(cast('MISSING_COUNT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column MISSING_COUNT, the last max_length value is 5. The average for this metric is 5.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_count__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('max_length' as varchar)\n and upper(column_name) = upper(cast('MISSING_COUNT' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Max Length", "metrics": [{"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "53dd458c0faed06b70f5344dbae8aaf7", "metric_id": "af4cb9609390382f295934f8d70d1345", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "4c9f473f313fb00a43950dcf4b96c272", "metric_id": "48147d1393a5e5ff636d6e139c26b6dc", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "dea7ae96d0b7522dc3aecb62ca52c879", "metric_id": "4c9d5aed92f6133bb5a15fc11dae99fa", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "2b5b4a10b2816f9fe77ecce0d836dfb3", "metric_id": "f2e894bca340e96e0f20f271efb5a76d", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "e4a8c6bbb522a9912a71b31399eb1089", "metric_id": "df5ac67888f118073510558401c8d1bf", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "ab23e204571501dcaf5ceeee9c80f018", "metric_id": "68d9f9cc5223e7bb004786120d3349a6", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "209b27dfaa2ce1770eb0f05089fdb01f", "metric_id": "d7ae84584a274eb055c74ac482e8811c", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "483a9f87dbfc7c2c53f9ece760d8b062", "metric_id": "1a78939f034558871da65cda885a102a", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "279c5f036b395838e1e055fbd80c5abd", "metric_id": "b42f8e173d3f0c171fcecb4a10c2260e", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "65e85002c13963f56ef66060f5a2e44b", "metric_id": "ccd916ac053e09f46401023c414b602c", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "67b3e3752c9a5d9dcaa3c88924e65289", "metric_id": "75de2106b365a86917ec66460962e589", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "0235680cb503cf44c6eab5b5ff26b937", "metric_id": "a93d0abe6ef384f0e74449426ee1d8fb", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "003902b3e4fdf010d62ee0e89bdf3605", "metric_id": "b08420b24d47837e7e62eb118f267c64", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "258745ab79c4c648d1848caca1a9c154", "metric_id": "fa627c45e8f4fd6f6627a03524bae826", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "3bb6220f56e8d8b872a611093e0ac3be", "metric_id": "b762828839cf5b9b99f73ab1de438891", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "16d3e10b96c26dc182967553732ab538", "metric_id": "298fa791532957a368b065e3dfb69df8", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "8657dfad620eb73bde8e6bc381b8f7e8", "metric_id": "419453b4d70f2a829b929fbe8452ca4e", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "d0e7db5049b015f838b9ff54e4a8b66f", "metric_id": "09a9829e6b1d8ee981eec3a820c9c3e2", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "376ae88219e273545839e72ba782fcd7", "metric_id": "403022c0222b82e528ce420c17f5770c", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "d084772db7737f4c4ccd05e4c67d21ee", "metric_id": "bde4e67427423640feb0134e52f4f0bd", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "f2653c70b0229f910ee093310213fa51", "metric_id": "fba5a2f341b323a537270eb08923b942", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "49deb764f2166406326524f1eab15eda", "metric_id": "fc3720a8c873e4a6c51a78978b1d01b9", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "6a843938d47d2d5a03857f0fa085f0c0", "metric_id": "7223035b68dd6fd7b52a3d600a754f02", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "77a63a51bce006fe5618f2c040d1743b", "metric_id": "d135daa28d0b0efe7c2048cecc864fa3", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "f6f6ba6d34b1826702c944c6c2ed8010", "metric_id": "d833f0b87b5c19d167286f5b11817ca6", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "b41edd7c24b06c0454e05ec576b445d3", "metric_id": "fa921ff857dfc2c6a97e00c347c0748d", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "ea4d19fd7d7dc805de6a645d38c2ed8a", "metric_id": "a3881ca265cdd8a7f235fecc3f8e7f80", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "75cef20a188695d4fc6c42cd1f7fc302", "metric_id": "4924c0f0cb84afcefe9ab55423a5f1a1", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "93f825e861900743a728dcbf02965745", "metric_id": "94ed09b9b261d2c5ec34644d78e67909", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}], "result_description": "In column MISSING_COUNT, the last max_length value is 5. The average for this metric is 5."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "string_column_anomalies", "column_name": "MIN_LENGTH", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:43:59+02:00", "latest_run_time_utc": "2023-01-02T10:43:59+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.elon_test.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "missing_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('missing_count' as varchar)\n and upper(column_name) = upper(cast('MIN_LENGTH' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column MIN_LENGTH, the last missing_count value is 0. The average for this metric is 0.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('missing_count' as varchar)\n and upper(column_name) = upper(cast('MIN_LENGTH' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Missing Count", "metrics": [{"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "e27a241d195868468aa5630d9ef69be5", "metric_id": "ce074eb8aa23d71dcdd09cd11df8dd1f", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last missing_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "c78f29fafaa7ff2017314c4a8d9553f1", "metric_id": "2b6562457c7e928145fd090a4a575291", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last missing_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "5aa77ea7d2bab05d96a47e100cecf206", "metric_id": "0f23c568376ccbf35d4a5a0d5addb488", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last missing_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "73121e1bda861f5efb869af1b4fa7bd0", "metric_id": "3772145b88627458e476329763c20898", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last missing_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "e5c0815b5433192f1431d7d44778a3ba", "metric_id": "b1eebd17d3c0d3f83675e31e52db851d", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last missing_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "54044e7bb5764536ba1a94c157e4b1a6", "metric_id": "a4c031ad08ff5913c816478011d9f415", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last missing_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "a27c8d3ec5e4b2a78045f2013b421f6a", "metric_id": "23d2601cfdc833455a58f29bd2e023d8", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last missing_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "24644439163fc219c07fc1f58164fcc0", "metric_id": "c12b2563c7361eef149a2f348aef90bf", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last missing_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "8203fb7666f78b248af836552deb8941", "metric_id": "3fd061b6b5d8be0dd0a11981faaef5a2", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last missing_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "20cd413c3cf8904a93b131da58484cb7", "metric_id": "fb44fc6832fa58ea8418b2c1eb06eff3", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last missing_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "c1f5346979985dab9b2e01ce258ccc0c", "metric_id": "8cce7ad57a6f50e6d51ac797b1a4c42a", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last missing_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "64fc282639d04135a2c86ef7d6279c43", "metric_id": "1d6d5c7d8e467733c53b61afc796fc0e", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last missing_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "f993c483504015e9847a9bdd4e7a42af", "metric_id": "1ca3ce5ba7841be5a765608d9287f47b", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last missing_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "a02782c1025759e6d7a44074b634ac20", "metric_id": "7857cd068c16e327f2828d7762d8465f", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last missing_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "50a9ccb1fb2be5a43ec3d2a972fb2332", "metric_id": "4a1e1118c97ee7d30115bd533cf76046", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last missing_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "97380509fde4c774e037900b882bc484", "metric_id": "4ef10aa48acfe5623e23b9f89aa0b11d", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last missing_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "44517286317b0bc2514efc558db16ee5", "metric_id": "cbb950ada1402711b77559fd226419fa", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last missing_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "2f6b6031de029fd1bc6cab53b43b3e20", "metric_id": "132bb9df45d447bb24702d4b365be821", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last missing_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "fae0097832fde8e36d3edcf45e031b87", "metric_id": "6e456a1e7fec2c4c7e98a4aae0ef3008", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last missing_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "1ae573c09944144a76c0d64da1220234", "metric_id": "2cc4c58931e20c04a2d42da9a003493d", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last missing_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "1d7a687d7fd02f84004ff51ccac22cb1", "metric_id": "49f6e90fd79501cd377206c5b2ae3e55", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last missing_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "766438dd5ef8730de051f0aa5b901b3b", "metric_id": "416d2d74f39b7f2c273dbf10b38cddda", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last missing_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "00a9f6dbab2dc21105a84572a023f725", "metric_id": "839cc453993acde36a6a04d6c0b46d20", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last missing_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "7160d46bffbfca7bc44cb26d9ae00002", "metric_id": "9770898feab27f5894f00a8f36d5ae92", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last missing_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "debcfa711847a9af77c69b8f3640f46b", "metric_id": "fde14086584e001e3a30c2327f7142b6", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last missing_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "cba95c065412b16e732c924e02cd01cd", "metric_id": "150acb356d6a925da17080bb31becfad", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last missing_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "bc9272670849f45cb79c6e9b3c38a3d4", "metric_id": "8f39fa9fe881073074fa5ed1892c60fe", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last missing_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "fd68206b4ca1598dce812a87677a9848", "metric_id": "7f378e98e7b65df802f488b7f28ec711", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last missing_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "6895f3b3a9ca35a960db1a81a53fb583", "metric_id": "fc5138bea71d8477efcbf686990ec8f4", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last missing_count value is 0. The average for this metric is 0.", "is_anomalous": false}], "result_description": "In column MIN_LENGTH, the last missing_count value is 0. The average for this metric is 0."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "string_column_anomalies", "column_name": "MISSING_COUNT", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:00+02:00", "latest_run_time_utc": "2023-01-02T10:44:00+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.elon_test.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "average_length", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_count__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average_length' as varchar)\n and upper(column_name) = upper(cast('MISSING_COUNT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column MISSING_COUNT, the last average_length value is 5. The average for this metric is 5.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_count__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average_length' as varchar)\n and upper(column_name) = upper(cast('MISSING_COUNT' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Average Length", "metrics": [{"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "e2386cee56646bc7937262c6d51402bc", "metric_id": "bd28c7c49d17ec689683ad3f91fd63e5", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "25f106a844656cc01ffb53595179cd89", "metric_id": "604484f3baa316a957d7b5ea88e92a0b", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "78867a211fbed877c6dc798bb1b1f448", "metric_id": "b77356902fc66535b8b8fbfe54830fcb", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "de9912f3963cf86b9bcdd0eedff1baca", "metric_id": "4788947c8a53c9afc721611062282450", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "8a66fa3e071d3ae409cf8b30e78cd19c", "metric_id": "c91d5f31e5c815c536a1d77151b738d1", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "81772db315f26a76b77bf58dd46254af", "metric_id": "4b88e71fdc82ce5e9b81661db0bd652a", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "66535bc100250ab8507f869225fd51c2", "metric_id": "46906a34a6e68333f5047fa7835846da", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "b5328a2362e22a2d1f3b7f1560fb094d", "metric_id": "ddd348f96b7d5070f23fc4bcf0f35167", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "ae6ff21ac8e81d72d91aa11e5e5e7485", "metric_id": "88de5657bdcdc49f94ca48461f9354f2", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "e37d2f26071b1bdb16ab6694d509c1ef", "metric_id": "85fbe2c8ddc2b73261d80d8ebaa749da", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "88c0de848f951e76ac2ac45bf926a2bc", "metric_id": "7f49c152f9970e5eb7bdd1c7c6fa2d62", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "555f9eb29ae31418fb28e0e596f36a8c", "metric_id": "471bfb0540ee9bb26a550485b442d4ab", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "eb1a24fd46fd7c28568606ee37940ef6", "metric_id": "e763689a4a4b9d11492f50514f6dc85e", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "3f59b9e43d63b05a887f51aeff73c5ae", "metric_id": "61619017d6101bb6697144ffaeb60078", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "2a109a666c6171edf073f07175ae4ba5", "metric_id": "e1c8ee7f987730af2bfc94dee3ee11e4", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "86c6abae19bbb7bf84a825e618b77c03", "metric_id": "a6b7f2392a80d6a58ff768a69276a483", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "309160f33487e8abe08078ab70c87361", "metric_id": "d8dd0301eb8e8efec2ee51018251509a", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "9d879ddf604396e639ddb9906878d250", "metric_id": "a8aba51b94374b855808d62135c412a0", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "461e8dc339592e0cdfa0399070c29e20", "metric_id": "ba2af7580cbeebdb7c943fab5cf46322", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "d7774cb74a5cb9272debc36a6b28c0e4", "metric_id": "8cc9b063ad7b81e5d172d87b77d98e04", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "caa475ddf70c7dd1a9ec8d1ed23139d9", "metric_id": "ea850546f3ff19f7281a65410c57937d", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "02be1d0e7c461476c88d104ffe3db9fd", "metric_id": "e8af23f4f67bf3aa408aacbbe1a94f08", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "54dd9324b152c90b0f4bf7d0c04b2d34", "metric_id": "c61e16f816d466ef2ae960b550694356", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "e63efdec6882dbcdc691a0e4386338ad", "metric_id": "1415e6c7d0bfc5dd8bfe4ea78b70ea4b", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "462bef84fceaa58053aa05f99714adcb", "metric_id": "64d0bfc3f5d487ba18d13abd8e4c14a9", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "641ee5750a4ad2269d00ebd5c45fce32", "metric_id": "a35bc7019c067372e141f5aba854e7ec", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "bf6fadf278381c15c1df42f1a5170f68", "metric_id": "37da2cf14889c2c6e81b9a9d89bf31cf", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "646869552bce5b772ec33f887b1cb362", "metric_id": "b3021acb0334d9cedc1c37832269870d", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "7701f05d5d9efdbbde0657db58eb15e7", "metric_id": "92c49edb88d39c385bf8b9cb2c5bfb61", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}], "result_description": "In column MISSING_COUNT, the last average_length value is 5. The average for this metric is 5."}}, {"metadata": {"test_unique_id": "model.elementary_integration_tests.string_column_anomalies.elementary_freshness_anomalies_string_column_anomalies_.freshness_anomalies", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "string_column_anomalies", "column_name": null, "test_name": "freshness_anomalies", "test_display_name": "Freshness Anomalies", "latest_run_time": "2023-01-02T12:42:19+02:00", "latest_run_time_utc": "2023-01-02T10:42:19+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.elon_test.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "freshness", "test_query": "select * from (\n \n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_FRESHNESS_ANOMALIES_STRING_COLUMN_ANOMALIES___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n\n \n\n\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('freshness' as varchar)", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "Last update was at 1969-12-30 00:00:00.000, 48 hours ago. Usually the table is updated within 24.8 hours.", "result_query": "select * from (\n \n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_FRESHNESS_ANOMALIES_STRING_COLUMN_ANOMALIES___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n\n \n\n\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('freshness' as varchar)"}, "configuration": {"test_name": "freshness_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Freshness", "metrics": [{"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "43dec59077db0610630fa8a5016740c9", "metric_id": "63cafd0dc10abf7e5bdfa56969a19526", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "detected_at": "2023-01-02T10:42:18.949000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-03 00:00:00.000", "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-03 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "d3623780ec5ae141f08e8851276aa4b2", "metric_id": "32edbd7fe81b730115438258283eb685", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "detected_at": "2023-01-02T10:42:18.949000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-04 00:00:00.000", "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-04 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "2c5095d84c51b1db373a286b6359c33b", "metric_id": "be61ff0405ca677e4e97a268717655fc", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "detected_at": "2023-01-02T10:42:18.949000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-05 00:00:00.000", "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-05 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "e8ce19cf962b7c10163081f0eb76a08a", "metric_id": "f0f0f60ac7f97f2b02434cb9c66901be", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "detected_at": "2023-01-02T10:42:18.949000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-06 00:00:00.000", "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-06 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "0b0f217e4c977b2d639506a455105390", "metric_id": "affbe7324c191b8e6bf58f7e82c1460e", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "detected_at": "2023-01-02T10:42:18.949000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-07 00:00:00.000", "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-07 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "d9db8c5a6bfc325b4464d8f15bc7e2be", "metric_id": "a9bb99d1d170d098bafba479b1a06fa8", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "detected_at": "2023-01-02T10:42:18.949000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-08 00:00:00.000", "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-08 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "c972c62ecc332c7b27f2fc039aa00bdb", "metric_id": "a81e533065b0f76f3f8d75b177974f54", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "detected_at": "2023-01-02T10:42:18.949000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-09 00:00:00.000", "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-09 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "bcaaca0af1801ceba9225fc877300d0c", "metric_id": "32407a0e287e4a5aef468851e49f036f", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "detected_at": "2023-01-02T10:42:18.949000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-10 00:00:00.000", "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-10 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "2961c3ca5c77f07f55df2c1b83b78c9a", "metric_id": "e93c16300bfe8e38e0f28fdbddf0aca2", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "detected_at": "2023-01-02T10:42:18.949000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-11 00:00:00.000", "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-11 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "ba81f1383aecfd546c2c49dc86cf4fe4", "metric_id": "f5edb3b15bbf5ba6bbe1f9a3edea8791", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "detected_at": "2023-01-02T10:42:18.949000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-12 00:00:00.000", "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-12 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "953955d8389e12fa079f7e0429b94356", "metric_id": "f0d56f651dae6f24ec7f471817abc0b9", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "detected_at": "2023-01-02T10:42:18.949000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-13 00:00:00.000", "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-13 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "9279bcd6f38f4498e1b7d5dc6727bfb4", "metric_id": "89e630f8857dcce9cc6ec16182c955fe", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "detected_at": "2023-01-02T10:42:18.949000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-14 00:00:00.000", "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-14 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "5cb7af5bd53a8f8ddfe98b0244b10fb7", "metric_id": "e40510fe7b592ede53992fdcd2d5682c", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "detected_at": "2023-01-02T10:42:18.949000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-15 00:00:00.000", "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-15 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "f5a59b817bdf0e1baffed94991aa6075", "metric_id": "ea2aba5a1073cd7cfec38314d06ac875", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "detected_at": "2023-01-02T10:42:18.949000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-16 00:00:00.000", "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-16 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "dd3000eb7935723463d575e3e4b505b5", "metric_id": "b2fd2159ad16733334b628c34933ef8b", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "detected_at": "2023-01-02T10:42:18.949000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-17 00:00:00.000", "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-17 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "27bbb20ce78c7d0091334d0ed9198e6c", "metric_id": "17832d76339a4dbe1b32febec0712130", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "detected_at": "2023-01-02T10:42:18.949000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-18 00:00:00.000", "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-18 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "154ee0236601f756bc917b5fc7c5d322", "metric_id": "2a6c11e0e63fca10cbbff503080664e7", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "detected_at": "2023-01-02T10:42:18.949000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-19 00:00:00.000", "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-19 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "d96d810369a48652a26adad7e76a9967", "metric_id": "129e9b65b5095010ffbca41ebf8bfd7d", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "detected_at": "2023-01-02T10:42:18.949000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-20 00:00:00.000", "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-20 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "6ba2921184307f1b163e2575748c343d", "metric_id": "6ebf62caf525909fca358bef2acaf4cd", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "detected_at": "2023-01-02T10:42:18.949000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-21 00:00:00.000", "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-21 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "66d04ecb8b383a5034286e0e2da9631a", "metric_id": "487a5747307cc087dc7c0aced7ae317a", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "detected_at": "2023-01-02T10:42:18.949000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-22 00:00:00.000", "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-22 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "4a9811be526e42e762305fb49abe75e3", "metric_id": "1a7b463bbbcd7dbc7f4d028c9ed16087", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "detected_at": "2023-01-02T10:42:18.949000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-23 00:00:00.000", "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-23 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "efba4ed154698c9e775841e94f858e98", "metric_id": "572c582b3fdd8b9b5b75f7cd66f6dc0c", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "detected_at": "2023-01-02T10:42:18.949000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-24 00:00:00.000", "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-24 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "ac21d5c3e711975f016f3b74c38a44fc", "metric_id": "517c554a44828cf9e125021058e0da96", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "detected_at": "2023-01-02T10:42:18.949000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-25 00:00:00.000", "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-25 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "81b309d4400a1f9b65fd5c88fe07a32b", "metric_id": "2e34549a2c050fda0bcb42b17896892e", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "detected_at": "2023-01-02T10:42:18.949000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-26 00:00:00.000", "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-26 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "2854b99682591c3e37b8d5d397bb937d", "metric_id": "efdfe1c0ac13d6e2461d9bf80608ee1c", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "detected_at": "2023-01-02T10:42:18.949000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-27 00:00:00.000", "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-27 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "18d070f5e7f4d2d9de16d6d9491c1f5d", "metric_id": "432c2182477f99a70064238112650cf9", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "detected_at": "2023-01-02T10:42:18.949000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-28 00:00:00.000", "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-28 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "35363385825ae1d06d42fa34307b9c61", "metric_id": "636a6bc3b72e8ad327899c37545e3465", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "detected_at": "2023-01-02T10:42:18.949000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-29 00:00:00.000", "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-29 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "07d9669b6e4b07368cc25b3dd4e9f4aa", "metric_id": "6439b8ed33e01b9c48e25ee20c5fd8f3", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "detected_at": "2023-01-02T10:42:18.949000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-30 00:00:00.000", "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-30 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 172800.0, "average": 89280.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "a1513c40abf11a79ea8c27baed672818", "metric_id": "a9a9e347f79029d672c9884a7ac81110", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "detected_at": "2023-01-02T10:42:18.949000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": null, "metric_name": "freshness", "anomaly_score": 5.294651389, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-30 00:00:00.000", "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 172800.0, "min_metric_value": 41956.771031554, "max_metric_value": 136603.228968446, "training_avg": 89280.0, "training_stddev": 15774.409656149, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-30 00:00:00.000, 48 hours ago. Usually the table is updated within 24.8 hours.", "is_anomalous": true}], "result_description": "Last update was at 1969-12-30 00:00:00.000, 48 hours ago. Usually the table is updated within 24.8 hours."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "string_column_anomalies", "column_name": "AVERAGE_LENGTH", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:43:59+02:00", "latest_run_time_utc": "2023-01-02T10:43:59+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.elon_test.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('AVERAGE_LENGTH' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column AVERAGE_LENGTH, the last null_count value is 0. The average for this metric is 0.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('AVERAGE_LENGTH' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Null Count", "metrics": [{"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "8341960deed23e2bae3ab53c87e6c7d6", "metric_id": "63c04b4b516f88dd027764277ecafebf", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "947384039877fd8e1be58b106ea91781", "metric_id": "906a3726da8ec9db7352eb09132bb639", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "0474d84d5fbf83f5bde406d7e9681863", "metric_id": "1913b3b46552035f46d5f547c0187f55", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "d25c5fe002f6eecda3448623cbecd1e8", "metric_id": "568621ce8c902516f90f51ecb9859226", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "3922b57ba53e7bd506a0e6d11cd4c24b", "metric_id": "34261bec4b4fe6f25a14c3eff31170dd", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "b3e349387b12ba2110868c24fc1fc434", "metric_id": "15e38bb75d427bbb1d772bd5e2009b54", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "03edcc8d109868de78cfcda1f51e32ab", "metric_id": "50b2407ba9afa3b8554595206250f936", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "ccca9336a82ba489cff313ec948fa439", "metric_id": "d08ecba21f1930431e49f3d7212fa5e7", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "a87372a7a9fa283fa354b142dc679e34", "metric_id": "b681c272d07aa689cdf8d5bc9e54e308", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "d679abf4eacfe85bd4c1050c7f8d2c91", "metric_id": "cb8f253de6cf47d35883f097e22c1f31", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "03431e726d80682b944b041907d3b32e", "metric_id": "52ce11a8fa48f9d106d3351e515c499c", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "bedc5641501dae1237f5bcd37e2fbb7b", "metric_id": "319cc163cdceb52a53d9d60b6404180b", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "15c7480a1d2315623a471883ca4be711", "metric_id": "e90bceb61f9cd0c9ce88eab4c84764cc", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "b3f96ddf1e8034d4a61d143765e57f1e", "metric_id": "4b8176afd481e7e083e6a4680d6fb117", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "2d76f6d25c415997e1a83412de509422", "metric_id": "5abdf49ec10af5d6392c991fb9362bf7", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "b77dc0a96c366da8c1b1453526366842", "metric_id": "95faac3fbe22f39af5e60134f2dc95e2", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "54556ebd034aecd5b0477ced63a30ac2", "metric_id": "8eafbf1efae068533ca213925f5f9dd2", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "889c0fe80635f1bc7b4faf34f12430fb", "metric_id": "ab4136488b5150c57270c184c1b6c912", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "49c438d53fd935a503abf7bc824c169a", "metric_id": "c8de167f7041c168a8bd33c84260388b", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "b0319cdbdaa9dfac0f4a6fad909249ce", "metric_id": "a7840211698aa8b9b51a6063f07f2f18", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "db6cba6fdb549201914ccf7b4e4cedff", "metric_id": "a335d1f8cb48e3af240297dda11b8513", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "8d4c08ac22dbc1e30b2f4713f87e0ba0", "metric_id": "5c9f66a0c251b704f7892bcf497fe252", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "1099c90287ea70e5f387c71dad1e8fa4", "metric_id": "39b0993ae81159bcf6c1c0b4bdfd16fe", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "55db6504febbbc74f806dc8672776b29", "metric_id": "7e4d400b7bcc950996ab08975c7c37a3", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "6799a3f70b32838aeceba78e49eee55e", "metric_id": "5c5c5ce174b9ff8f0d5700161acf4219", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "6b16f44f8294ec51daad0bd2cee1c472", "metric_id": "c3f424804a35b958b2713a6a04a31fc7", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "b4546bde55d92677e4938af9ce2cfe66", "metric_id": "023bf552aaca4d2f57731c77725dc0e7", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "6421c1eb9d2fcf0b91c26cdf4e97f4f3", "metric_id": "4b58bc7e69ed7025a74b81e515e033c4", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}, {"value": 0.0, "average": 0.0, "min_value": 0.0, "max_value": 0.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "745e4bfcb7df560313f97f9f0c508702", "metric_id": "5d67e6e5ce5de7c5cd4edde97623dfe2", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 0.0, "min_metric_value": 0.0, "max_metric_value": 0.0, "training_avg": 0.0, "training_stddev": 0.0, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last null_count value is 0. The average for this metric is 0.", "is_anomalous": false}], "result_description": "In column AVERAGE_LENGTH, the last null_count value is 0. The average for this metric is 0."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_schema_changes_string_column_anomalies_.c57cd75b87", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "string_column_anomalies", "column_name": null, "test_name": "schema_changes", "test_display_name": "Schema Changes", "latest_run_time": "2023-01-02T12:46:17+02:00", "latest_run_time_utc": "2023-01-02T10:46:17+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.elon_test.string_column_anomalies", "test_type": "schema_change", "test_sub_type": "generic", "test_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.elementary_test_results\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n \n\n \n \n \n\n \n \n \n\n \n \n \n\n \n\n \n \n \n \n select * from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_SCHEMA_CHANGES_STRING_COLUMN_ANOMALIES___SCHEMA_CHANGES_ALERTS\"", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.elementary_test_results\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n \n\n \n \n \n\n \n \n \n\n \n \n \n\n \n\n \n \n \n \n select * from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_SCHEMA_CHANGES_STRING_COLUMN_ANOMALIES___SCHEMA_CHANGES_ALERTS\""}, "configuration": {"test_name": "schema_changes", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "string_column_anomalies", "column_name": "MISSING_PERCENT", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:00+02:00", "latest_run_time_utc": "2023-01-02T10:44:00+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.elon_test.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('MISSING_PERCENT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column MISSING_PERCENT, the last null_count value is 64. The average for this metric is 21.867.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('MISSING_PERCENT' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Null Count", "metrics": [{"value": 19.0, "average": 19.0, "min_value": 19.0, "max_value": 19.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "b4651499791b3bf75b91be5290ee899d", "metric_id": "b632d52c316e027fc5fcf2f67075cd4b", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 19.0, "min_metric_value": 19.0, "max_metric_value": 19.0, "training_avg": 19.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_count value is 19. The average for this metric is 19.", "is_anomalous": false}, {"value": 21.0, "average": 19.666666667, "min_value": 16.202565052, "max_value": 23.130768282, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "db8ee4555c1072e338c51a09a78a12f9", "metric_id": "9011a20b0137ad31a37868a701664b72", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_count", "anomaly_score": 1.154700538, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 21.0, "min_metric_value": 16.202565052, "max_metric_value": 23.130768282, "training_avg": 19.666666667, "training_stddev": 1.154700538, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_count value is 21. The average for this metric is 19.667.", "is_anomalous": false}, {"value": 17.0, "average": 19.0, "min_value": 14.101020514, "max_value": 23.898979486, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "531c67051603ec50578d5f24ee369166", "metric_id": "4244ecabef3259649df01aabc9d78a30", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_count", "anomaly_score": -1.224744871, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 17.0, "min_metric_value": 14.101020514, "max_metric_value": 23.898979486, "training_avg": 19.0, "training_stddev": 1.632993162, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_count value is 17. The average for this metric is 19.", "is_anomalous": false}, {"value": 18.0, "average": 18.8, "min_value": 14.350280908, "max_value": 23.249719092, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "a2fc9aab997d59451e8e080db6748166", "metric_id": "0bd456a4d65b6a7d752041c04c00d5a0", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_count", "anomaly_score": -0.53935989, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 18.0, "min_metric_value": 14.350280908, "max_metric_value": 23.249719092, "training_avg": 18.8, "training_stddev": 1.483239697, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_count value is 18. The average for this metric is 18.8.", "is_anomalous": false}, {"value": 29.0, "average": 20.5, "min_value": 7.38893597, "max_value": 33.61106403, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "73aebe8d1ebe4e0f1a25ce259a9f8cae", "metric_id": "f832ceca49c16bb96721055d1941251c", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_count", "anomaly_score": 1.944922238, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 29.0, "min_metric_value": 7.38893597, "max_metric_value": 33.61106403, "training_avg": 20.5, "training_stddev": 4.370354677, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_count value is 29. The average for this metric is 20.5.", "is_anomalous": false}, {"value": 23.0, "average": 20.857142857, "min_value": 8.557317074, "max_value": 33.15696864, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "2202f8f50349053cfc0e1c452a72f115", "metric_id": "19b77db742636180e7e9b5df614a47f6", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_count", "anomaly_score": 0.5226554865, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 23.0, "min_metric_value": 8.557317074, "max_metric_value": 33.15696864, "training_avg": 20.857142857, "training_stddev": 4.099941928, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_count value is 23. The average for this metric is 20.857.", "is_anomalous": false}, {"value": 22.0, "average": 21.0, "min_value": 9.548237815, "max_value": 32.451762185, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "cb98dc99829e3e6d38673ff29fa82830", "metric_id": "dd67140b5d2566200f9a56c395b94307", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_count", "anomaly_score": 0.261968416, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 22.0, "min_metric_value": 9.548237815, "max_metric_value": 32.451762185, "training_avg": 21.0, "training_stddev": 3.817254062, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_count value is 22. The average for this metric is 21.", "is_anomalous": false}, {"value": 18.0, "average": 20.666666667, "min_value": 9.542368936, "max_value": 31.790964397, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "157bc133a42e9811750872ece70817a6", "metric_id": "89ab9a103f70badaf9d0240982c82920", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_count", "anomaly_score": -0.71914652, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 18.0, "min_metric_value": 9.542368936, "max_metric_value": 31.790964397, "training_avg": 20.666666667, "training_stddev": 3.708099244, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_count value is 18. The average for this metric is 20.667.", "is_anomalous": false}, {"value": 20.0, "average": 20.6, "min_value": 10.092859571, "max_value": 31.107140429, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "64a82364014a444682f4e6b174fea1c9", "metric_id": "0b8d01ca95d2c1168e8004d505827881", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_count", "anomaly_score": -0.1713120722, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 20.0, "min_metric_value": 10.092859571, "max_metric_value": 31.107140429, "training_avg": 20.6, "training_stddev": 3.502380143, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_count value is 20. The average for this metric is 20.6.", "is_anomalous": false}, {"value": 23.0, "average": 20.818181818, "min_value": 10.616577666, "max_value": 31.01978597, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "ee38dc19b5f85dfbc2511357d7d77320", "metric_id": "ab8b97961e9504d98f8038f7dfb9c6ee", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_count", "anomaly_score": 0.641610324, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 23.0, "min_metric_value": 10.616577666, "max_metric_value": 31.01978597, "training_avg": 20.818181818, "training_stddev": 3.400534717, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_count value is 23. The average for this metric is 20.818.", "is_anomalous": false}, {"value": 17.0, "average": 20.5, "min_value": 10.226468254, "max_value": 30.773531746, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "561638b23232fc28df280a90d2c75e6a", "metric_id": "d6bd7a556ac2fd6f88d22132abcd2f1d", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_count", "anomaly_score": -1.022043856, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 17.0, "min_metric_value": 10.226468254, "max_metric_value": 30.773531746, "training_avg": 20.5, "training_stddev": 3.424510582, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_count value is 17. The average for this metric is 20.5.", "is_anomalous": false}, {"value": 21.0, "average": 20.538461538, "min_value": 10.693509689, "max_value": 30.383413388, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "0e9544aedc6a26a0f6530af6a11e4b29", "metric_id": "29d9edd08c2aa9422090e48ae43977cc", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_count", "anomaly_score": 0.1406421693, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 21.0, "min_metric_value": 10.693509689, "max_metric_value": 30.383413388, "training_avg": 20.538461538, "training_stddev": 3.281650617, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_count value is 21. The average for this metric is 20.538.", "is_anomalous": false}, {"value": 13.0, "average": 20.0, "min_value": 8.77502784, "max_value": 31.22497216, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "48dabd6f90b78768df3b97204e979bce", "metric_id": "3624465c9823d3f166acde866c464791", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_count", "anomaly_score": -1.870828693, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 13.0, "min_metric_value": 8.77502784, "max_metric_value": 31.22497216, "training_avg": 20.0, "training_stddev": 3.741657387, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_count value is 13. The average for this metric is 20.", "is_anomalous": false}, {"value": 15.0, "average": 19.666666667, "min_value": 8.177541374, "max_value": 31.15579196, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "6fb4e6cc69db4537dc8f2e61bb2a99e1", "metric_id": "511380259a4468edc804ac85acb003b8", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_count", "anomaly_score": -1.218543592, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 15.0, "min_metric_value": 8.177541374, "max_metric_value": 31.15579196, "training_avg": 19.666666667, "training_stddev": 3.829708431, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_count value is 15. The average for this metric is 19.667.", "is_anomalous": false}, {"value": 20.0, "average": 19.6875, "min_value": 8.585135387, "max_value": 30.789864613, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "f3223b6eca09183adb37114679518761", "metric_id": "bd7593fc4164a31ae7f5178ed5df6fea", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_count", "anomaly_score": 0.08444147105, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 20.0, "min_metric_value": 8.585135387, "max_metric_value": 30.789864613, "training_avg": 19.6875, "training_stddev": 3.700788204, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_count value is 20. The average for this metric is 19.688.", "is_anomalous": false}, {"value": 26.0, "average": 20.058823529, "min_value": 8.368895738, "max_value": 31.748751321, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "f2c446ac8c2ec71d3ad5bad3267a8dbb", "metric_id": "b1f9008ca8399bfb10b658f26b973df5", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_count", "anomaly_score": 1.524691147, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 26.0, "min_metric_value": 8.368895738, "max_metric_value": 31.748751321, "training_avg": 20.058823529, "training_stddev": 3.896642597, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_count value is 26. The average for this metric is 20.059.", "is_anomalous": false}, {"value": 18.0, "average": 19.944444444, "min_value": 8.51049088, "max_value": 31.378398009, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "78ced693b321cf3e92dbaff0339d7087", "metric_id": "c76a2cd89d6393d58c3f2d331e5b3855", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_count", "anomaly_score": -0.5101764058, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 18.0, "min_metric_value": 8.51049088, "max_metric_value": 31.378398009, "training_avg": 19.944444444, "training_stddev": 3.811317855, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_count value is 18. The average for this metric is 19.944.", "is_anomalous": false}, {"value": 26.0, "average": 20.263157895, "min_value": 8.395466238, "max_value": 32.130849552, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "0671bf706ca985be04807b28eae877ea", "metric_id": "cb8ca80c29db9d43af5f6fb5cf479274", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_count", "anomaly_score": 1.450199989, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 26.0, "min_metric_value": 8.395466238, "max_metric_value": 32.130849552, "training_avg": 20.263157895, "training_stddev": 3.955897219, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_count value is 26. The average for this metric is 20.263.", "is_anomalous": false}, {"value": 24.0, "average": 20.45, "min_value": 8.62996794, "max_value": 32.27003206, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "21d3c9de6a63b9a6055998c9d9505ca5", "metric_id": "cb492c6dedc646a066e327e01d45ff04", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_count", "anomaly_score": 0.9010127846, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 24.0, "min_metric_value": 8.62996794, "max_metric_value": 32.27003206, "training_avg": 20.45, "training_stddev": 3.940010687, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_count value is 24. The average for this metric is 20.45.", "is_anomalous": false}, {"value": 26.0, "average": 20.714285714, "min_value": 8.634195838, "max_value": 32.794375591, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "2aed92a991f70b8695eb53b97aaf41b3", "metric_id": "ed010f4e6fbf08306afe4ea3016c17a6", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_count", "anomaly_score": 1.312667622, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 26.0, "min_metric_value": 8.634195838, "max_metric_value": 32.794375591, "training_avg": 20.714285714, "training_stddev": 4.026696626, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_count value is 26. The average for this metric is 20.714.", "is_anomalous": false}, {"value": 19.0, "average": 20.636363636, "min_value": 8.796523343, "max_value": 32.47620393, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "b27fdacf97f61948982c0a951a679980", "metric_id": "9364a53bf47a90b1ee3046170e0f4085", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_count", "anomaly_score": -0.4146247574, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 19.0, "min_metric_value": 8.796523343, "max_metric_value": 32.47620393, "training_avg": 20.636363636, "training_stddev": 3.946613431, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_count value is 19. The average for this metric is 20.636.", "is_anomalous": false}, {"value": 23.0, "average": 20.739130435, "min_value": 9.077396111, "max_value": 32.400864758, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "a86146b86bdc5656ba0f71a34e9a682d", "metric_id": "31d082a9a3ecebd5d55dd87ecf3c1af1", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_count", "anomaly_score": 0.5816123492, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 23.0, "min_metric_value": 9.077396111, "max_metric_value": 32.400864758, "training_avg": 20.739130435, "training_stddev": 3.887244774, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_count value is 23. The average for this metric is 20.739.", "is_anomalous": false}, {"value": 18.0, "average": 20.625, "min_value": 9.096915201, "max_value": 32.153084799, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "cd6ef51b480f471012ce15d1b890fe5c", "metric_id": "33d019429b55e3efa06aef68d38b13b5", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_count", "anomaly_score": -0.6831143366, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 18.0, "min_metric_value": 9.096915201, "max_metric_value": 32.153084799, "training_avg": 20.625, "training_stddev": 3.842694933, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_count value is 18. The average for this metric is 20.625.", "is_anomalous": false}, {"value": 24.0, "average": 20.76, "min_value": 9.294399274, "max_value": 32.225600726, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "d0663f1f3930b33d846e90a8d3c5027a", "metric_id": "a4efaa25aa1500c347a4993f8673fb40", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_count", "anomaly_score": 0.8477532257, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 24.0, "min_metric_value": 9.294399274, "max_metric_value": 32.225600726, "training_avg": 20.76, "training_stddev": 3.821866909, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_count value is 24. The average for this metric is 20.76.", "is_anomalous": false}, {"value": 18.0, "average": 20.653846154, "min_value": 9.303143024, "max_value": 32.004549284, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "89f1d471003a1d5190bfba5102b4d105", "metric_id": "948f6eb55aa6cdbcc1bf76b395002cf3", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_count", "anomaly_score": -0.7014136808, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 18.0, "min_metric_value": 9.303143024, "max_metric_value": 32.004549284, "training_avg": 20.653846154, "training_stddev": 3.78356771, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_count value is 18. The average for this metric is 20.654.", "is_anomalous": false}, {"value": 14.0, "average": 20.407407407, "min_value": 8.632815031, "max_value": 32.181999784, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "82ff8ab93d8f3cf8a78f508293fb55a9", "metric_id": "be3713717f56bf234485233b25235bb2", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_count", "anomaly_score": -1.632517, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 14.0, "min_metric_value": 8.632815031, "max_metric_value": 32.181999784, "training_avg": 20.407407407, "training_stddev": 3.924864126, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_count value is 14. The average for this metric is 20.407.", "is_anomalous": false}, {"value": 18.0, "average": 20.321428571, "min_value": 8.686608091, "max_value": 31.956249052, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "40e904ab9e2f0e250a1e8bf7f8f45dd1", "metric_id": "3ff9a328a11dda1c7e7cd5fb3afc42b1", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_count", "anomaly_score": -0.5985726832, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 18.0, "min_metric_value": 8.686608091, "max_metric_value": 31.956249052, "training_avg": 20.321428571, "training_stddev": 3.878273494, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_count value is 18. The average for this metric is 20.321.", "is_anomalous": false}, {"value": 23.0, "average": 20.413793103, "min_value": 8.891593498, "max_value": 31.935992709, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "ad47989d86f9ff96a394638ae9819c6f", "metric_id": "a5ff352e5f498c8fa39a1904415267f4", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_count", "anomaly_score": 0.6733628088, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 23.0, "min_metric_value": 8.891593498, "max_metric_value": 31.935992709, "training_avg": 20.413793103, "training_stddev": 3.840733202, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_count value is 23. The average for this metric is 20.414.", "is_anomalous": false}, {"value": 64.0, "average": 21.866666667, "min_value": 8.891593498, "max_value": 31.935992709, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "c99fbb4aa7c86814dffda0b5e9590ddb", "metric_id": "d63bfa0db644a04e5bc1211157a1eb3b", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_count", "anomaly_score": 4.783932433, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 64.0, "min_metric_value": -4.555111185, "max_metric_value": 48.288444518, "training_avg": 21.866666667, "training_stddev": 8.807259284, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_count value is 64. The average for this metric is 21.867.", "is_anomalous": true}], "result_description": "In column MISSING_PERCENT, the last null_count value is 64. The average for this metric is 21.867."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "string_column_anomalies", "column_name": "MISSING_COUNT", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:00+02:00", "latest_run_time_utc": "2023-01-02T10:44:00+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.elon_test.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "missing_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_count__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('missing_count' as varchar)\n and upper(column_name) = upper(cast('MISSING_COUNT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column MISSING_COUNT, the last missing_count value is 20. The average for this metric is 3.567.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_count__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('missing_count' as varchar)\n and upper(column_name) = upper(cast('MISSING_COUNT' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Missing Count", "metrics": [{"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "0c676048180dd85a70d4fec5c45f21d5", "metric_id": "3015601e7c4e8b988407388efbba0d1c", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "db6037b3b66cfbe7de206f9dcac0ae79", "metric_id": "8ece148cf07aa87173131d82deb74967", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "9231c71feeb74c180c69bebb47d7155b", "metric_id": "b24255dd775813ca8415fb802903c93c", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "38dd3b5765a2381ed2c86b67c68735f1", "metric_id": "33732c3bcea25eb17135132e3dfac7ee", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "43c57e3994be665964253a68c3ad2301", "metric_id": "c69f827b629f5d4e176c396b214a5e80", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "7d7b3448aa11da91968a0fd332b52995", "metric_id": "23d1ecb9941fb16103e622d0307b4541", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "33252e614b84568287c63cdb29084548", "metric_id": "ec4178bf583dbd2687005649f1794e39", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "0f499f1477bb6b68b282f4f322ab25aa", "metric_id": "6232fe94512e5eae1e152d12ad9e741c", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "bdee0402ad1cc19fd062e5972e7ef39a", "metric_id": "9a875717f7b253b837c67fbf7fa0a77e", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "4076877d1161393e8f80971f7515e2fa", "metric_id": "a4a4b7018fd953d035f29e1ef764f4ed", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "f0a405fdaa1e4d1ce8caf44708176f09", "metric_id": "2785ed243f236a3da32e9de8426a0000", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "89024fc7a0b17a13df375f17f75661d1", "metric_id": "afd365a724492be75e2728df5e925bef", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "7c6477f6a6874170fd3525508d7d2fb8", "metric_id": "6fd6005396fa17abd3762654e02b73a4", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "aec0e5da7485b08d8f45604236a1a48e", "metric_id": "29709c05e13c7f6bc160261537081c0f", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "0478496a7078aaa4f8d191560032ebeb", "metric_id": "469f05ac5de01a0ffd954501f1103f0a", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "1bc770717e53abf7832f983f976ab0b7", "metric_id": "101b16dca9673c84019a0393fb90dc78", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "3218015f4c5cd6e5fad3d1f2594a8279", "metric_id": "03b89339c310d4557dfdf6dfdfaeb67b", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "0be246f47868710bb54686c2f13a2e40", "metric_id": "e75719f5a0dce05e77f56bf5fa4f5006", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "64da2e5df42b1abfad67393ff6bbe98c", "metric_id": "65bef9e3743910abe817729f856b0ca2", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "8f8f18c4b4a0457948f5a030fe918324", "metric_id": "992fcfdcc8afc4d14cce7e739fcaac8e", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "4b0e843a439625168cfb647addb1be77", "metric_id": "23721e53b691038bdfcdc3b16d875b4c", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "ecd3a7faccfdf3037b2a0423e9584508", "metric_id": "90074951f5d284a84f3d06cca8038a7c", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "cc79ef25d56a849a2c662b9467d8af6d", "metric_id": "41d4404d8822b93fc2b4fe85b9955846", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "b6a37df0ee82f4dd6dc970d168abf797", "metric_id": "df14035fe30b84d19a3664416d5980fe", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "41a83f23551b29fb0803e8a0232b2b06", "metric_id": "3a603d351c302250bbe6830aade3e4ea", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "3923fef480560f2f3f601185445adaab", "metric_id": "5cc9f1682aed983e2bb2bd811519b47f", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "0f7e1984f98548dd8477ef6015882232", "metric_id": "a6ec7c6d7a87d00939814fa5738cc5fa", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "01307983c7e72985756ca60564686eea", "metric_id": "a20c5172b4d2232ad3c393eed2f5f879", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 20.0, "average": 3.566666667, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "eb2f3d0192cca5fae77a5fc29cc0c5b4", "metric_id": "4a6d08761a18250b69b580552cdc97c2", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "missing_count", "anomaly_score": 5.294651389, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 20.0, "min_metric_value": -5.744616811, "max_metric_value": 12.877950144, "training_avg": 3.566666667, "training_stddev": 3.103761159, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last missing_count value is 20. The average for this metric is 3.567.", "is_anomalous": true}], "result_description": "In column MISSING_COUNT, the last missing_count value is 20. The average for this metric is 3.567."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "string_column_anomalies", "column_name": "MIN_LENGTH", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:43:59+02:00", "latest_run_time_utc": "2023-01-02T10:43:59+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.elon_test.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "max_length", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('max_length' as varchar)\n and upper(column_name) = upper(cast('MIN_LENGTH' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column MIN_LENGTH, the last max_length value is 10. The average for this metric is 10.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('max_length' as varchar)\n and upper(column_name) = upper(cast('MIN_LENGTH' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Max Length", "metrics": [{"value": 10.0, "average": 10.0, "min_value": 10.0, "max_value": 10.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "5c6c3ce714d687fffec1a15cad345f92", "metric_id": "8008237e95ef3c5399eda5616602e75a", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 10.0, "min_metric_value": 10.0, "max_metric_value": 10.0, "training_avg": 10.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last max_length value is 10. The average for this metric is 10.", "is_anomalous": false}, {"value": 10.0, "average": 10.0, "min_value": 10.0, "max_value": 10.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "3f96672b3ad2c334bb633d00eca9ef16", "metric_id": "9c143f6a68d52179c07507b6721b48f5", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 10.0, "min_metric_value": 10.0, "max_metric_value": 10.0, "training_avg": 10.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last max_length value is 10. The average for this metric is 10.", "is_anomalous": false}, {"value": 10.0, "average": 10.0, "min_value": 10.0, "max_value": 10.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "e1cdea3ec8eaec5e74e5b4c1993a2bb7", "metric_id": "5f37b960795faf355ab4dde27e345277", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 10.0, "min_metric_value": 10.0, "max_metric_value": 10.0, "training_avg": 10.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last max_length value is 10. The average for this metric is 10.", "is_anomalous": false}, {"value": 10.0, "average": 10.0, "min_value": 10.0, "max_value": 10.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "67b93d96e65732585ab0bb5c6e1c56c4", "metric_id": "e59d2b015301d42a116101e02f0e536f", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 10.0, "min_metric_value": 10.0, "max_metric_value": 10.0, "training_avg": 10.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last max_length value is 10. The average for this metric is 10.", "is_anomalous": false}, {"value": 10.0, "average": 10.0, "min_value": 10.0, "max_value": 10.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "a3ecd681d67260dca3e081e13ff87402", "metric_id": "00c064ac5bccd573c83e280818e49bb3", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 10.0, "min_metric_value": 10.0, "max_metric_value": 10.0, "training_avg": 10.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last max_length value is 10. The average for this metric is 10.", "is_anomalous": false}, {"value": 10.0, "average": 10.0, "min_value": 10.0, "max_value": 10.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "231eef7b0cadfcf7a4103666f56f9737", "metric_id": "9828a8d1f50fe7172004ef1753c0c623", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 10.0, "min_metric_value": 10.0, "max_metric_value": 10.0, "training_avg": 10.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last max_length value is 10. The average for this metric is 10.", "is_anomalous": false}, {"value": 10.0, "average": 10.0, "min_value": 10.0, "max_value": 10.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "747d72a3717229881856e876507403ae", "metric_id": "2ee260ebaf7a9094b9667eb78d9062df", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 10.0, "min_metric_value": 10.0, "max_metric_value": 10.0, "training_avg": 10.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last max_length value is 10. The average for this metric is 10.", "is_anomalous": false}, {"value": 10.0, "average": 10.0, "min_value": 10.0, "max_value": 10.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "d9ceada2ce90ecff674974e840e129f3", "metric_id": "fc14c16ce75a9671832e1cb5965e3c7b", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 10.0, "min_metric_value": 10.0, "max_metric_value": 10.0, "training_avg": 10.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last max_length value is 10. The average for this metric is 10.", "is_anomalous": false}, {"value": 10.0, "average": 10.0, "min_value": 10.0, "max_value": 10.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "8bd4cc1604356c434d753369b4523cf2", "metric_id": "cbcff33b9742e13af70dce55d780bbba", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 10.0, "min_metric_value": 10.0, "max_metric_value": 10.0, "training_avg": 10.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last max_length value is 10. The average for this metric is 10.", "is_anomalous": false}, {"value": 10.0, "average": 10.0, "min_value": 10.0, "max_value": 10.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "1bdbcea401fd5302c5b0101e27059b8c", "metric_id": "0326f0078fe6cf8aa9325a3364c2dfbd", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 10.0, "min_metric_value": 10.0, "max_metric_value": 10.0, "training_avg": 10.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last max_length value is 10. The average for this metric is 10.", "is_anomalous": false}, {"value": 10.0, "average": 10.0, "min_value": 10.0, "max_value": 10.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "62ae4b7eb290d4153c0725f2fcc29695", "metric_id": "bb20b45b713e8484c0058a5456e5ae4d", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 10.0, "min_metric_value": 10.0, "max_metric_value": 10.0, "training_avg": 10.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last max_length value is 10. The average for this metric is 10.", "is_anomalous": false}, {"value": 10.0, "average": 10.0, "min_value": 10.0, "max_value": 10.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "712d35c54f13f97cb9050ce07b157fb5", "metric_id": "85881d134b88f680d2268a05e3a2fc38", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 10.0, "min_metric_value": 10.0, "max_metric_value": 10.0, "training_avg": 10.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last max_length value is 10. The average for this metric is 10.", "is_anomalous": false}, {"value": 10.0, "average": 10.0, "min_value": 10.0, "max_value": 10.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "76c05da70d2d9e017db71cbaf440a466", "metric_id": "8c6fc0797178eb697d9d2eccbb7baeab", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 10.0, "min_metric_value": 10.0, "max_metric_value": 10.0, "training_avg": 10.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last max_length value is 10. The average for this metric is 10.", "is_anomalous": false}, {"value": 10.0, "average": 10.0, "min_value": 10.0, "max_value": 10.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "9d9201cf62da3d30cebee29d27a77167", "metric_id": "76545a49dbc5099ef7c5c8ae40612162", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 10.0, "min_metric_value": 10.0, "max_metric_value": 10.0, "training_avg": 10.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last max_length value is 10. The average for this metric is 10.", "is_anomalous": false}, {"value": 10.0, "average": 10.0, "min_value": 10.0, "max_value": 10.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "ec893339ee9b8f7f68159f7e4164fa81", "metric_id": "8d634ba91277fea8bb85fba25eea26e1", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 10.0, "min_metric_value": 10.0, "max_metric_value": 10.0, "training_avg": 10.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last max_length value is 10. The average for this metric is 10.", "is_anomalous": false}, {"value": 10.0, "average": 10.0, "min_value": 10.0, "max_value": 10.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "e8cbda6d435ee6868d61ef6be09888ed", "metric_id": "8fb900719d06ad69fcd43623d3cbac9a", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 10.0, "min_metric_value": 10.0, "max_metric_value": 10.0, "training_avg": 10.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last max_length value is 10. The average for this metric is 10.", "is_anomalous": false}, {"value": 10.0, "average": 10.0, "min_value": 10.0, "max_value": 10.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "605f109a9d041bc40eb67bf9eccfe61c", "metric_id": "570ff3cc47e9a1335da29783a5e4a7f8", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 10.0, "min_metric_value": 10.0, "max_metric_value": 10.0, "training_avg": 10.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last max_length value is 10. The average for this metric is 10.", "is_anomalous": false}, {"value": 10.0, "average": 10.0, "min_value": 10.0, "max_value": 10.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "8ac3e6391f2d03a3075c561c011fc840", "metric_id": "c7d3575c464bd70196c0e6f032668d20", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 10.0, "min_metric_value": 10.0, "max_metric_value": 10.0, "training_avg": 10.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last max_length value is 10. The average for this metric is 10.", "is_anomalous": false}, {"value": 10.0, "average": 10.0, "min_value": 10.0, "max_value": 10.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "d0dcacc05ffb504e673e8c614f727f8a", "metric_id": "65ae8b74fffebc8669a546ffb0efbee8", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 10.0, "min_metric_value": 10.0, "max_metric_value": 10.0, "training_avg": 10.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last max_length value is 10. The average for this metric is 10.", "is_anomalous": false}, {"value": 10.0, "average": 10.0, "min_value": 10.0, "max_value": 10.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "e4b1140931bac08426ec522e4034cae5", "metric_id": "6424bb15896152c7e85749b2d2b67bc8", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 10.0, "min_metric_value": 10.0, "max_metric_value": 10.0, "training_avg": 10.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last max_length value is 10. The average for this metric is 10.", "is_anomalous": false}, {"value": 10.0, "average": 10.0, "min_value": 10.0, "max_value": 10.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "b5c5db238af7fad3ecb24b5b63c7c826", "metric_id": "963fafc7fa65af3781e5c060e1931db1", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 10.0, "min_metric_value": 10.0, "max_metric_value": 10.0, "training_avg": 10.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last max_length value is 10. The average for this metric is 10.", "is_anomalous": false}, {"value": 10.0, "average": 10.0, "min_value": 10.0, "max_value": 10.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "e3a24452ce7e6b50ef127c4571a044ef", "metric_id": "5e70059fee0bca774f7310f013cab770", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 10.0, "min_metric_value": 10.0, "max_metric_value": 10.0, "training_avg": 10.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last max_length value is 10. The average for this metric is 10.", "is_anomalous": false}, {"value": 10.0, "average": 10.0, "min_value": 10.0, "max_value": 10.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "db68db213ccffb260604128a639a4c84", "metric_id": "957601fab4666de5388fcddb21bc94d5", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 10.0, "min_metric_value": 10.0, "max_metric_value": 10.0, "training_avg": 10.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last max_length value is 10. The average for this metric is 10.", "is_anomalous": false}, {"value": 10.0, "average": 10.0, "min_value": 10.0, "max_value": 10.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "dbc63bd74b750513b2866542bfcffa9d", "metric_id": "08bf8a1aed7f49fa4332a211d0b63496", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 10.0, "min_metric_value": 10.0, "max_metric_value": 10.0, "training_avg": 10.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last max_length value is 10. The average for this metric is 10.", "is_anomalous": false}, {"value": 10.0, "average": 10.0, "min_value": 10.0, "max_value": 10.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "7eb21cc4d96db6ed6e351b6c00bf0667", "metric_id": "2f69fffd54e49a3f3aae8fc759c17e93", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 10.0, "min_metric_value": 10.0, "max_metric_value": 10.0, "training_avg": 10.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last max_length value is 10. The average for this metric is 10.", "is_anomalous": false}, {"value": 10.0, "average": 10.0, "min_value": 10.0, "max_value": 10.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "ee8e45e70a80cae6bd40909ed680ac8f", "metric_id": "9bcd6a89cd19bb8dee6f1336c265aaa9", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 10.0, "min_metric_value": 10.0, "max_metric_value": 10.0, "training_avg": 10.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last max_length value is 10. The average for this metric is 10.", "is_anomalous": false}, {"value": 10.0, "average": 10.0, "min_value": 10.0, "max_value": 10.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "73be53a5adee13dda36de80982e04d7d", "metric_id": "c0932f35141a3c374aa8b0b0cf84614b", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 10.0, "min_metric_value": 10.0, "max_metric_value": 10.0, "training_avg": 10.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last max_length value is 10. The average for this metric is 10.", "is_anomalous": false}, {"value": 10.0, "average": 10.0, "min_value": 10.0, "max_value": 10.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "2ee2fdd874a74743909d01ffbb343fe7", "metric_id": "99fdde6778bcc72f51cf589e5c287ce0", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 10.0, "min_metric_value": 10.0, "max_metric_value": 10.0, "training_avg": 10.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last max_length value is 10. The average for this metric is 10.", "is_anomalous": false}, {"value": 10.0, "average": 10.0, "min_value": 10.0, "max_value": 10.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "55f05693da4bd96275ecdb955fb329d5", "metric_id": "f89a27c4c9af87b89a82a6d369611673", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 10.0, "min_metric_value": 10.0, "max_metric_value": 10.0, "training_avg": 10.0, "training_stddev": 0.0, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last max_length value is 10. The average for this metric is 10.", "is_anomalous": false}], "result_description": "In column MIN_LENGTH, the last max_length value is 10. The average for this metric is 10."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "string_column_anomalies", "column_name": "MISSING_PERCENT", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:00+02:00", "latest_run_time_utc": "2023-01-02T10:44:00+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.elon_test.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "missing_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('missing_count' as varchar)\n and upper(column_name) = upper(cast('MISSING_PERCENT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column MISSING_PERCENT, the last missing_count value is 64. The average for this metric is 21.867.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('missing_count' as varchar)\n and upper(column_name) = upper(cast('MISSING_PERCENT' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Missing Count", "metrics": [{"value": 19.0, "average": 19.0, "min_value": 19.0, "max_value": 19.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "fb9c8bf6197d4e272499e14287b27fdb", "metric_id": "ebc881740836b7c10f4a3a87d8514711", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 19.0, "min_metric_value": 19.0, "max_metric_value": 19.0, "training_avg": 19.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_count value is 19. The average for this metric is 19.", "is_anomalous": false}, {"value": 21.0, "average": 19.666666667, "min_value": 16.202565052, "max_value": 23.130768282, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "72f598c6f3b7ab834eea31b098695c21", "metric_id": "a5473722e46c6c5cc46351e7ce153f3f", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_count", "anomaly_score": 1.154700538, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 21.0, "min_metric_value": 16.202565052, "max_metric_value": 23.130768282, "training_avg": 19.666666667, "training_stddev": 1.154700538, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_count value is 21. The average for this metric is 19.667.", "is_anomalous": false}, {"value": 17.0, "average": 19.0, "min_value": 14.101020514, "max_value": 23.898979486, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "fb6d5afbf7a52207c8ba28c046e19d12", "metric_id": "33b724af758b6ba594561499ca87dcea", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_count", "anomaly_score": -1.224744871, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 17.0, "min_metric_value": 14.101020514, "max_metric_value": 23.898979486, "training_avg": 19.0, "training_stddev": 1.632993162, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_count value is 17. The average for this metric is 19.", "is_anomalous": false}, {"value": 18.0, "average": 18.8, "min_value": 14.350280908, "max_value": 23.249719092, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "5b1b4a46ebac070c04bb226da42ba948", "metric_id": "f4a48daab5c21a73bbb7a3eec021c939", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_count", "anomaly_score": -0.53935989, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 18.0, "min_metric_value": 14.350280908, "max_metric_value": 23.249719092, "training_avg": 18.8, "training_stddev": 1.483239697, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_count value is 18. The average for this metric is 18.8.", "is_anomalous": false}, {"value": 29.0, "average": 20.5, "min_value": 7.38893597, "max_value": 33.61106403, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "4c95fcdd1070520dcc5545a9425decd6", "metric_id": "167c0341d601fd93aa920a2831d16047", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_count", "anomaly_score": 1.944922238, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 29.0, "min_metric_value": 7.38893597, "max_metric_value": 33.61106403, "training_avg": 20.5, "training_stddev": 4.370354677, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_count value is 29. The average for this metric is 20.5.", "is_anomalous": false}, {"value": 23.0, "average": 20.857142857, "min_value": 8.557317074, "max_value": 33.15696864, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "8a651a32badba21778b43e8bb0941188", "metric_id": "a9e602edeee1de18f0dd2d06ead57dce", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_count", "anomaly_score": 0.5226554865, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 23.0, "min_metric_value": 8.557317074, "max_metric_value": 33.15696864, "training_avg": 20.857142857, "training_stddev": 4.099941928, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_count value is 23. The average for this metric is 20.857.", "is_anomalous": false}, {"value": 22.0, "average": 21.0, "min_value": 9.548237815, "max_value": 32.451762185, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "d32f4a3ef6929c58a499879280bbb8fd", "metric_id": "962bc9e2a26742feabab2ea22e18f4cb", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_count", "anomaly_score": 0.261968416, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 22.0, "min_metric_value": 9.548237815, "max_metric_value": 32.451762185, "training_avg": 21.0, "training_stddev": 3.817254062, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_count value is 22. The average for this metric is 21.", "is_anomalous": false}, {"value": 18.0, "average": 20.666666667, "min_value": 9.542368936, "max_value": 31.790964397, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "f314d5103a501df8c78fd8a7772ff270", "metric_id": "aacc205e42ecff12e3d7112a7de12293", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_count", "anomaly_score": -0.71914652, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 18.0, "min_metric_value": 9.542368936, "max_metric_value": 31.790964397, "training_avg": 20.666666667, "training_stddev": 3.708099244, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_count value is 18. The average for this metric is 20.667.", "is_anomalous": false}, {"value": 20.0, "average": 20.6, "min_value": 10.092859571, "max_value": 31.107140429, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "1679b5c1423fd99a7ce4c20256768ec2", "metric_id": "71ea5d82c68e348a1b3b292038b59965", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_count", "anomaly_score": -0.1713120722, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 20.0, "min_metric_value": 10.092859571, "max_metric_value": 31.107140429, "training_avg": 20.6, "training_stddev": 3.502380143, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_count value is 20. The average for this metric is 20.6.", "is_anomalous": false}, {"value": 23.0, "average": 20.818181818, "min_value": 10.616577666, "max_value": 31.01978597, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "a827a70d23f97d01eda1843567f79afd", "metric_id": "68eaba3a271bd047590f9263a732cf5f", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_count", "anomaly_score": 0.641610324, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 23.0, "min_metric_value": 10.616577666, "max_metric_value": 31.01978597, "training_avg": 20.818181818, "training_stddev": 3.400534717, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_count value is 23. The average for this metric is 20.818.", "is_anomalous": false}, {"value": 17.0, "average": 20.5, "min_value": 10.226468254, "max_value": 30.773531746, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "7567915e22b04e2c00f7d0a53f3df771", "metric_id": "9349de19da8d841ef11995dfb74c1794", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_count", "anomaly_score": -1.022043856, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 17.0, "min_metric_value": 10.226468254, "max_metric_value": 30.773531746, "training_avg": 20.5, "training_stddev": 3.424510582, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_count value is 17. The average for this metric is 20.5.", "is_anomalous": false}, {"value": 21.0, "average": 20.538461538, "min_value": 10.693509689, "max_value": 30.383413388, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "a0d1b9be9f1b794b878791858e620bbd", "metric_id": "88a0d8bb132020e0e791d48035b25efd", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_count", "anomaly_score": 0.1406421693, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 21.0, "min_metric_value": 10.693509689, "max_metric_value": 30.383413388, "training_avg": 20.538461538, "training_stddev": 3.281650617, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_count value is 21. The average for this metric is 20.538.", "is_anomalous": false}, {"value": 13.0, "average": 20.0, "min_value": 8.77502784, "max_value": 31.22497216, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "e336dbf60819b52428353606f373cc3f", "metric_id": "d107a1bb31131d5341788b9de67bdacf", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_count", "anomaly_score": -1.870828693, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 13.0, "min_metric_value": 8.77502784, "max_metric_value": 31.22497216, "training_avg": 20.0, "training_stddev": 3.741657387, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_count value is 13. The average for this metric is 20.", "is_anomalous": false}, {"value": 15.0, "average": 19.666666667, "min_value": 8.177541374, "max_value": 31.15579196, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "f099d2aa22ddd58c6e7b21949d1a08dd", "metric_id": "81cb2054633fc9710d091248038271ac", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_count", "anomaly_score": -1.218543592, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 15.0, "min_metric_value": 8.177541374, "max_metric_value": 31.15579196, "training_avg": 19.666666667, "training_stddev": 3.829708431, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_count value is 15. The average for this metric is 19.667.", "is_anomalous": false}, {"value": 20.0, "average": 19.6875, "min_value": 8.585135387, "max_value": 30.789864613, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "186bed9387aa911d42658afd6357d24d", "metric_id": "1abc244a0f820353eb0525d50def275b", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_count", "anomaly_score": 0.08444147105, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 20.0, "min_metric_value": 8.585135387, "max_metric_value": 30.789864613, "training_avg": 19.6875, "training_stddev": 3.700788204, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_count value is 20. The average for this metric is 19.688.", "is_anomalous": false}, {"value": 26.0, "average": 20.058823529, "min_value": 8.368895738, "max_value": 31.748751321, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "2e9e8b939d4bd01ee6bcd03943421382", "metric_id": "b2617c3d35010cb7e19c0035b9d4b22d", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_count", "anomaly_score": 1.524691147, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 26.0, "min_metric_value": 8.368895738, "max_metric_value": 31.748751321, "training_avg": 20.058823529, "training_stddev": 3.896642597, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_count value is 26. The average for this metric is 20.059.", "is_anomalous": false}, {"value": 18.0, "average": 19.944444444, "min_value": 8.51049088, "max_value": 31.378398009, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "549b7d5b5ad625b15cd5e9778c31e1bd", "metric_id": "5a76ade892e62a7a4cbc18d01e9a2a00", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_count", "anomaly_score": -0.5101764058, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 18.0, "min_metric_value": 8.51049088, "max_metric_value": 31.378398009, "training_avg": 19.944444444, "training_stddev": 3.811317855, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_count value is 18. The average for this metric is 19.944.", "is_anomalous": false}, {"value": 26.0, "average": 20.263157895, "min_value": 8.395466238, "max_value": 32.130849552, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "cf6bb778f89638a78cc645ea3e6fed55", "metric_id": "25a204987b3ea0bac54b3e910791eaf2", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_count", "anomaly_score": 1.450199989, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 26.0, "min_metric_value": 8.395466238, "max_metric_value": 32.130849552, "training_avg": 20.263157895, "training_stddev": 3.955897219, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_count value is 26. The average for this metric is 20.263.", "is_anomalous": false}, {"value": 24.0, "average": 20.45, "min_value": 8.62996794, "max_value": 32.27003206, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "c4e5e02e2bc7bbea488f5b2ba0c2b8a0", "metric_id": "df96ab9676f33e8cad2388e308913e75", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_count", "anomaly_score": 0.9010127846, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 24.0, "min_metric_value": 8.62996794, "max_metric_value": 32.27003206, "training_avg": 20.45, "training_stddev": 3.940010687, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_count value is 24. The average for this metric is 20.45.", "is_anomalous": false}, {"value": 26.0, "average": 20.714285714, "min_value": 8.634195838, "max_value": 32.794375591, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "b4a8ccb0818d50a7ed3b5df2a3aea9a4", "metric_id": "3fd8ad543952c36aadaa54e1b65fba9c", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_count", "anomaly_score": 1.312667622, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 26.0, "min_metric_value": 8.634195838, "max_metric_value": 32.794375591, "training_avg": 20.714285714, "training_stddev": 4.026696626, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_count value is 26. The average for this metric is 20.714.", "is_anomalous": false}, {"value": 19.0, "average": 20.636363636, "min_value": 8.796523343, "max_value": 32.47620393, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "ea62d53d9f094d9fe57770a9617b267d", "metric_id": "15fe17903c7c5efb790a1d98edc4e4f5", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_count", "anomaly_score": -0.4146247574, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 19.0, "min_metric_value": 8.796523343, "max_metric_value": 32.47620393, "training_avg": 20.636363636, "training_stddev": 3.946613431, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_count value is 19. The average for this metric is 20.636.", "is_anomalous": false}, {"value": 23.0, "average": 20.739130435, "min_value": 9.077396111, "max_value": 32.400864758, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "f34eac429d0e3341487e6899f7fc485c", "metric_id": "a906ba954a1f473f0afcd678fe6be85a", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_count", "anomaly_score": 0.5816123492, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 23.0, "min_metric_value": 9.077396111, "max_metric_value": 32.400864758, "training_avg": 20.739130435, "training_stddev": 3.887244774, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_count value is 23. The average for this metric is 20.739.", "is_anomalous": false}, {"value": 18.0, "average": 20.625, "min_value": 9.096915201, "max_value": 32.153084799, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "2ad8fb6a3b8aa4ee559eac16327b9fea", "metric_id": "f99f2d4866cf26b85c6946950adfe89b", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_count", "anomaly_score": -0.6831143366, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 18.0, "min_metric_value": 9.096915201, "max_metric_value": 32.153084799, "training_avg": 20.625, "training_stddev": 3.842694933, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_count value is 18. The average for this metric is 20.625.", "is_anomalous": false}, {"value": 24.0, "average": 20.76, "min_value": 9.294399274, "max_value": 32.225600726, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "7041dab674a8808a7d04280a5b60a773", "metric_id": "d6fd0b8d81344fad924a637f26b9b985", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_count", "anomaly_score": 0.8477532257, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 24.0, "min_metric_value": 9.294399274, "max_metric_value": 32.225600726, "training_avg": 20.76, "training_stddev": 3.821866909, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_count value is 24. The average for this metric is 20.76.", "is_anomalous": false}, {"value": 18.0, "average": 20.653846154, "min_value": 9.303143024, "max_value": 32.004549284, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "6955e8441bbc45e02dc5ba809d5e9c38", "metric_id": "2f81e548d4cd76c50109d2f74c88725f", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_count", "anomaly_score": -0.7014136808, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 18.0, "min_metric_value": 9.303143024, "max_metric_value": 32.004549284, "training_avg": 20.653846154, "training_stddev": 3.78356771, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_count value is 18. The average for this metric is 20.654.", "is_anomalous": false}, {"value": 14.0, "average": 20.407407407, "min_value": 8.632815031, "max_value": 32.181999784, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "e67ae2bbfcb2e5807b2645638557d901", "metric_id": "f410741666aa05c1fcc977cd8ffac5fc", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_count", "anomaly_score": -1.632517, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 14.0, "min_metric_value": 8.632815031, "max_metric_value": 32.181999784, "training_avg": 20.407407407, "training_stddev": 3.924864126, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_count value is 14. The average for this metric is 20.407.", "is_anomalous": false}, {"value": 18.0, "average": 20.321428571, "min_value": 8.686608091, "max_value": 31.956249052, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "63c91de3eb1170033ef8cc7cd228e237", "metric_id": "f8d484ca14d374eda2640cfa2de0fd0e", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_count", "anomaly_score": -0.5985726832, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 18.0, "min_metric_value": 8.686608091, "max_metric_value": 31.956249052, "training_avg": 20.321428571, "training_stddev": 3.878273494, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_count value is 18. The average for this metric is 20.321.", "is_anomalous": false}, {"value": 23.0, "average": 20.413793103, "min_value": 8.891593498, "max_value": 31.935992709, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "be28bcb3c311ee5d200e9dbcdd328a98", "metric_id": "bd302309dddc3bfaa79acea6d01179de", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_count", "anomaly_score": 0.6733628088, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 23.0, "min_metric_value": 8.891593498, "max_metric_value": 31.935992709, "training_avg": 20.413793103, "training_stddev": 3.840733202, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_count value is 23. The average for this metric is 20.414.", "is_anomalous": false}, {"value": 64.0, "average": 21.866666667, "min_value": 8.891593498, "max_value": 31.935992709, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "a2cc191c40c9bfcdc0c46dd658fbcaec", "metric_id": "2fb0c5c85db3e9dc274842f87aabca06", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_count", "anomaly_score": 4.783932433, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 64.0, "min_metric_value": -4.555111185, "max_metric_value": 48.288444518, "training_avg": 21.866666667, "training_stddev": 8.807259284, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_count value is 64. The average for this metric is 21.867.", "is_anomalous": true}], "result_description": "In column MISSING_PERCENT, the last missing_count value is 64. The average for this metric is 21.867."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "string_column_anomalies", "column_name": "MISSING_PERCENT", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:00+02:00", "latest_run_time_utc": "2023-01-02T10:44:00+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.elon_test.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('MISSING_PERCENT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column MISSING_PERCENT, the last null_percent value is 64. The average for this metric is 21.867.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('MISSING_PERCENT' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Null Percent", "metrics": [{"value": 19.0, "average": 19.0, "min_value": 19.0, "max_value": 19.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "5836991346c98c0ae2868e51b5d757e4", "metric_id": "07f9d321cc940ef155c655497bee62da", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 19.0, "min_metric_value": 19.0, "max_metric_value": 19.0, "training_avg": 19.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_percent value is 19. The average for this metric is 19.", "is_anomalous": false}, {"value": 21.0, "average": 19.666666667, "min_value": 16.202565052, "max_value": 23.130768282, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "c53c2927fc187c0ee2345f6e14f65dc9", "metric_id": "d974c5eda17c1fd366fefc24099ee4b6", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_percent", "anomaly_score": 1.154700538, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 21.0, "min_metric_value": 16.202565052, "max_metric_value": 23.130768282, "training_avg": 19.666666667, "training_stddev": 1.154700538, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_percent value is 21. The average for this metric is 19.667.", "is_anomalous": false}, {"value": 17.0, "average": 19.0, "min_value": 14.101020514, "max_value": 23.898979486, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "940d2792b6924a56157ba0c28006a4a1", "metric_id": "1b8c42ed11d8a4cd3416eeafcd8ab286", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_percent", "anomaly_score": -1.224744871, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 17.0, "min_metric_value": 14.101020514, "max_metric_value": 23.898979486, "training_avg": 19.0, "training_stddev": 1.632993162, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_percent value is 17. The average for this metric is 19.", "is_anomalous": false}, {"value": 18.0, "average": 18.8, "min_value": 14.350280908, "max_value": 23.249719092, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "62890dc08673580ef04acab0af0a4366", "metric_id": "79ce16b2a01bf732f28d6c92778418dd", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_percent", "anomaly_score": -0.53935989, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 18.0, "min_metric_value": 14.350280908, "max_metric_value": 23.249719092, "training_avg": 18.8, "training_stddev": 1.483239697, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_percent value is 18. The average for this metric is 18.8.", "is_anomalous": false}, {"value": 29.0, "average": 20.5, "min_value": 7.38893597, "max_value": 33.61106403, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "314ec979986f3a89966dd239c463a7fb", "metric_id": "f37481b31d5aa55ee6a7697ba86d3683", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_percent", "anomaly_score": 1.944922238, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 29.0, "min_metric_value": 7.38893597, "max_metric_value": 33.61106403, "training_avg": 20.5, "training_stddev": 4.370354677, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_percent value is 29. The average for this metric is 20.5.", "is_anomalous": false}, {"value": 23.0, "average": 20.857142857, "min_value": 8.557317074, "max_value": 33.15696864, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "92893916006fd003a0a029da73239251", "metric_id": "990967290931857c962bcd0af25630a2", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_percent", "anomaly_score": 0.5226554865, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 23.0, "min_metric_value": 8.557317074, "max_metric_value": 33.15696864, "training_avg": 20.857142857, "training_stddev": 4.099941928, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_percent value is 23. The average for this metric is 20.857.", "is_anomalous": false}, {"value": 22.0, "average": 21.0, "min_value": 9.548237815, "max_value": 32.451762185, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "173665c3f666abd8bd32399ce1a3ee88", "metric_id": "4cab83cfd74d208af311678eb3e049e1", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_percent", "anomaly_score": 0.261968416, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 22.0, "min_metric_value": 9.548237815, "max_metric_value": 32.451762185, "training_avg": 21.0, "training_stddev": 3.817254062, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_percent value is 22. The average for this metric is 21.", "is_anomalous": false}, {"value": 18.0, "average": 20.666666667, "min_value": 9.542368936, "max_value": 31.790964397, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "fbf7b4c5d00a4b793ddc822ccdbb3d88", "metric_id": "6ee6428eb5c60ee8e2601f9634c37e74", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_percent", "anomaly_score": -0.71914652, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 18.0, "min_metric_value": 9.542368936, "max_metric_value": 31.790964397, "training_avg": 20.666666667, "training_stddev": 3.708099244, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_percent value is 18. The average for this metric is 20.667.", "is_anomalous": false}, {"value": 20.0, "average": 20.6, "min_value": 10.092859571, "max_value": 31.107140429, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "3debce136122aba07560e548cc3c69a3", "metric_id": "3d97a923e438900d7f8743f49c70ede8", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_percent", "anomaly_score": -0.1713120722, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 20.0, "min_metric_value": 10.092859571, "max_metric_value": 31.107140429, "training_avg": 20.6, "training_stddev": 3.502380143, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_percent value is 20. The average for this metric is 20.6.", "is_anomalous": false}, {"value": 23.0, "average": 20.818181818, "min_value": 10.616577666, "max_value": 31.01978597, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "0c4a733a4b2186e16538eeba4f2464d6", "metric_id": "1348de5bdc6e4a7b34a37abf084bd0ff", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_percent", "anomaly_score": 0.641610324, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 23.0, "min_metric_value": 10.616577666, "max_metric_value": 31.01978597, "training_avg": 20.818181818, "training_stddev": 3.400534717, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_percent value is 23. The average for this metric is 20.818.", "is_anomalous": false}, {"value": 17.0, "average": 20.5, "min_value": 10.226468254, "max_value": 30.773531746, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "d73e84d6d045c411485b6072343b616d", "metric_id": "8b33b1ff70a48f571e67d0c8efe74d7d", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_percent", "anomaly_score": -1.022043856, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 17.0, "min_metric_value": 10.226468254, "max_metric_value": 30.773531746, "training_avg": 20.5, "training_stddev": 3.424510582, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_percent value is 17. The average for this metric is 20.5.", "is_anomalous": false}, {"value": 21.0, "average": 20.538461538, "min_value": 10.693509689, "max_value": 30.383413388, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "dbf60f1c171c3c98a1c9c9af735688bb", "metric_id": "83a9f5f973395b90d0f1c774b3b88f58", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_percent", "anomaly_score": 0.1406421693, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 21.0, "min_metric_value": 10.693509689, "max_metric_value": 30.383413388, "training_avg": 20.538461538, "training_stddev": 3.281650617, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_percent value is 21. The average for this metric is 20.538.", "is_anomalous": false}, {"value": 13.0, "average": 20.0, "min_value": 8.77502784, "max_value": 31.22497216, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "03747b40d537f0c1b817d29c9c49115d", "metric_id": "3bf8770243b8e21a383119f129f377c5", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_percent", "anomaly_score": -1.870828693, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 13.0, "min_metric_value": 8.77502784, "max_metric_value": 31.22497216, "training_avg": 20.0, "training_stddev": 3.741657387, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_percent value is 13. The average for this metric is 20.", "is_anomalous": false}, {"value": 15.0, "average": 19.666666667, "min_value": 8.177541374, "max_value": 31.15579196, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "05202b1794206aea53e3e76262f88a9e", "metric_id": "a1e027ca10820759261beefc73296306", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_percent", "anomaly_score": -1.218543592, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 15.0, "min_metric_value": 8.177541374, "max_metric_value": 31.15579196, "training_avg": 19.666666667, "training_stddev": 3.829708431, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_percent value is 15. The average for this metric is 19.667.", "is_anomalous": false}, {"value": 20.0, "average": 19.6875, "min_value": 8.585135387, "max_value": 30.789864613, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "e970252730927395b39317d20641553b", "metric_id": "24c8992806cd6603e3581416a95237d8", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_percent", "anomaly_score": 0.08444147105, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 20.0, "min_metric_value": 8.585135387, "max_metric_value": 30.789864613, "training_avg": 19.6875, "training_stddev": 3.700788204, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_percent value is 20. The average for this metric is 19.688.", "is_anomalous": false}, {"value": 26.0, "average": 20.058823529, "min_value": 8.368895738, "max_value": 31.748751321, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "74f06f3fd3d39411ffb45140adee70be", "metric_id": "13ef8e1bbd7218ff2c3c9b18bf3d26a7", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_percent", "anomaly_score": 1.524691147, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 26.0, "min_metric_value": 8.368895738, "max_metric_value": 31.748751321, "training_avg": 20.058823529, "training_stddev": 3.896642597, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_percent value is 26. The average for this metric is 20.059.", "is_anomalous": false}, {"value": 18.0, "average": 19.944444444, "min_value": 8.51049088, "max_value": 31.378398009, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "15d3eac584fae2e2d3b3cf78c1bda7c3", "metric_id": "60fb454725dc03696c908ab8f98acbca", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_percent", "anomaly_score": -0.5101764058, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 18.0, "min_metric_value": 8.51049088, "max_metric_value": 31.378398009, "training_avg": 19.944444444, "training_stddev": 3.811317855, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_percent value is 18. The average for this metric is 19.944.", "is_anomalous": false}, {"value": 26.0, "average": 20.263157895, "min_value": 8.395466238, "max_value": 32.130849552, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "0357d2d244f803a6d22bd7d197d1711f", "metric_id": "5c79c849d8c43c40fd53157b5b6ef485", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_percent", "anomaly_score": 1.450199989, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 26.0, "min_metric_value": 8.395466238, "max_metric_value": 32.130849552, "training_avg": 20.263157895, "training_stddev": 3.955897219, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_percent value is 26. The average for this metric is 20.263.", "is_anomalous": false}, {"value": 24.0, "average": 20.45, "min_value": 8.62996794, "max_value": 32.27003206, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "363ba3b3ca127237732febfa72e07ab6", "metric_id": "b266ddb2abbebf268c845213470ffaa8", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_percent", "anomaly_score": 0.9010127846, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 24.0, "min_metric_value": 8.62996794, "max_metric_value": 32.27003206, "training_avg": 20.45, "training_stddev": 3.940010687, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_percent value is 24. The average for this metric is 20.45.", "is_anomalous": false}, {"value": 26.0, "average": 20.714285714, "min_value": 8.634195838, "max_value": 32.794375591, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "f2ecf6e0ee0d43b93c5617dbc56d99e4", "metric_id": "8a70aaaa4034bf6d1a455984ae0bd725", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_percent", "anomaly_score": 1.312667622, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 26.0, "min_metric_value": 8.634195838, "max_metric_value": 32.794375591, "training_avg": 20.714285714, "training_stddev": 4.026696626, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_percent value is 26. The average for this metric is 20.714.", "is_anomalous": false}, {"value": 19.0, "average": 20.636363636, "min_value": 8.796523343, "max_value": 32.47620393, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "b01c566c7ee7d031cefbb2dd82f916fa", "metric_id": "4f9b942613848eff5918e1efa2c34f61", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_percent", "anomaly_score": -0.4146247574, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 19.0, "min_metric_value": 8.796523343, "max_metric_value": 32.47620393, "training_avg": 20.636363636, "training_stddev": 3.946613431, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_percent value is 19. The average for this metric is 20.636.", "is_anomalous": false}, {"value": 23.0, "average": 20.739130435, "min_value": 9.077396111, "max_value": 32.400864758, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "03716081346c23afd3fd7c04d6cda63c", "metric_id": "d046d623b105a121ff4844f30a3a825d", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_percent", "anomaly_score": 0.5816123492, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 23.0, "min_metric_value": 9.077396111, "max_metric_value": 32.400864758, "training_avg": 20.739130435, "training_stddev": 3.887244774, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_percent value is 23. The average for this metric is 20.739.", "is_anomalous": false}, {"value": 18.0, "average": 20.625, "min_value": 9.096915201, "max_value": 32.153084799, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "2c2bc094f1fe7ceb2f8bffb069d5108f", "metric_id": "4de7ac967799a0910a35b3013cf4b136", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_percent", "anomaly_score": -0.6831143366, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 18.0, "min_metric_value": 9.096915201, "max_metric_value": 32.153084799, "training_avg": 20.625, "training_stddev": 3.842694933, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_percent value is 18. The average for this metric is 20.625.", "is_anomalous": false}, {"value": 24.0, "average": 20.76, "min_value": 9.294399274, "max_value": 32.225600726, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "258cb7c25fc86d278d5fa72e5e65f5ec", "metric_id": "dfa28d1692623d51ad801d7a64e791fc", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_percent", "anomaly_score": 0.8477532257, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 24.0, "min_metric_value": 9.294399274, "max_metric_value": 32.225600726, "training_avg": 20.76, "training_stddev": 3.821866909, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_percent value is 24. The average for this metric is 20.76.", "is_anomalous": false}, {"value": 18.0, "average": 20.653846154, "min_value": 9.303143024, "max_value": 32.004549284, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "9656ce9ea7ffc960a71215c29a11b491", "metric_id": "2dcddfb6bc993ada6c2447cf8b303a6c", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_percent", "anomaly_score": -0.7014136808, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 18.0, "min_metric_value": 9.303143024, "max_metric_value": 32.004549284, "training_avg": 20.653846154, "training_stddev": 3.78356771, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_percent value is 18. The average for this metric is 20.654.", "is_anomalous": false}, {"value": 14.0, "average": 20.407407407, "min_value": 8.632815031, "max_value": 32.181999784, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "1ef35303b8fb003c57151d1fc16957e4", "metric_id": "69a1f6693734b5de2b30d5cb617b7167", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_percent", "anomaly_score": -1.632517, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 14.0, "min_metric_value": 8.632815031, "max_metric_value": 32.181999784, "training_avg": 20.407407407, "training_stddev": 3.924864126, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_percent value is 14. The average for this metric is 20.407.", "is_anomalous": false}, {"value": 18.0, "average": 20.321428571, "min_value": 8.686608091, "max_value": 31.956249052, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "9ffddb796f44a797f14e55c0b2414a39", "metric_id": "574a7a338b2d8c49ca1d0c41acdcc56c", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_percent", "anomaly_score": -0.5985726832, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 18.0, "min_metric_value": 8.686608091, "max_metric_value": 31.956249052, "training_avg": 20.321428571, "training_stddev": 3.878273494, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_percent value is 18. The average for this metric is 20.321.", "is_anomalous": false}, {"value": 23.0, "average": 20.413793103, "min_value": 8.891593498, "max_value": 31.935992709, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "30c39e345e302ebff599fb146452ddcb", "metric_id": "87a5ec68fa5a2966b85025a94df6ad6e", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_percent", "anomaly_score": 0.6733628088, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 23.0, "min_metric_value": 8.891593498, "max_metric_value": 31.935992709, "training_avg": 20.413793103, "training_stddev": 3.840733202, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_percent value is 23. The average for this metric is 20.414.", "is_anomalous": false}, {"value": 64.0, "average": 21.866666667, "min_value": 8.891593498, "max_value": 31.935992709, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "f4f6444843b23d18aef63d34210560fc", "metric_id": "68a450bcceaea422aad842a947d99961", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "null_percent", "anomaly_score": 4.783932433, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 64.0, "min_metric_value": -4.555111185, "max_metric_value": 48.288444518, "training_avg": 21.866666667, "training_stddev": 8.807259284, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last null_percent value is 64. The average for this metric is 21.867.", "is_anomalous": true}], "result_description": "In column MISSING_PERCENT, the last null_percent value is 64. The average for this metric is 21.867."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "string_column_anomalies", "column_name": "MISSING_COUNT", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:00+02:00", "latest_run_time_utc": "2023-01-02T10:44:00+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.elon_test.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_count__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('MISSING_COUNT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column MISSING_COUNT, the last null_percent value is 20. The average for this metric is 3.567.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_count__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('MISSING_COUNT' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Null Percent", "metrics": [{"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "8b81d17e28208e36bca8c7685e4be859", "metric_id": "a8551e900e78667e5281f6ef6ee802f9", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "ec652c7af4c4674d9c40b179bceb1438", "metric_id": "4ec433556a38d8a50502c8b2ff4f679f", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "25fbac1768cc8ec119636036be698d2e", "metric_id": "0b2cf58f39ff697d58c05c4a105ba6a4", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "5d129deaa97b4ac8809ea4e2ed68b86b", "metric_id": "bc7a5e85fe31ba07dbd0bdf4fa0dac9d", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "7df7aa6637027dc05f7b3213e113697b", "metric_id": "151db5d17088600ea5050c3d92bf2ba2", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "864fd82f2d0f09cd99b2a0db9cb46dc3", "metric_id": "b853c41b6cc8da3816f87f171c38df88", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "5f12fe66b97fb1f05ac017cecf647d08", "metric_id": "36c93c30d0f37c689ea84cce415cfaac", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "1e6bb86ed106b7294c66ec81bdda6874", "metric_id": "79842bcf753d2c6f367fe1a03674dbdb", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "646d31282421c431501ab9f2539ba49e", "metric_id": "212ac6cf3d83f8c9b73787f275abba3c", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "c0036cf6848c06d628d78f84f1841e1c", "metric_id": "45400374a28a80e49449e330f64ad5f0", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "d15a17defdda03cdf6588ab6171ea465", "metric_id": "4aca3334e53cfc2277986eb8ebcf97c6", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "de2842f2854d4a003924866fa48f93c7", "metric_id": "590341643ff9d184a041e01175cc93bc", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "49c7661bcb0099c8051b944ab47b1c98", "metric_id": "45b954ac334f1799a97f0e64dd29488a", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "144d43622ac5aa4f3bd5660235f2e2a4", "metric_id": "e19155d342e7272e584c8c8bf6ba5e8a", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "33273029062aec266c1b419317342a79", "metric_id": "d8b7f3dfeee04e4c5ce4bfa98a3aeaf5", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "ec57557eed5a4298d861d27602fb6e64", "metric_id": "7eea50c19e7cd8f330afc8833d0cc9e9", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "8a1834aa056614b75f0a1e42659f535d", "metric_id": "0130a995a5ecf6604f6d4c7a3aa338ea", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "75a511a1921f8b694be3f8765a8128dd", "metric_id": "cd796db448bf77699ad35c0bc09b27d5", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "5d8504707fccf8cf3b7d02b6ee8dda21", "metric_id": "47dc691cbd17fae8502e8b6ba5eeaaea", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "d8e3d8588ed815964b4362ab37efa7f6", "metric_id": "c9596fea8c1f1389f2d1a47e57b59ea1", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "1976b97a3156c58f12edace04be4aa8c", "metric_id": "1c1679f811a3968280b0fbc0fcf977c6", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "fb551dd836f690b3b681d41621fb2158", "metric_id": "058bb662ac38d54c409eb800cb4649eb", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "b17c5a0e008edae43a81f7141f3d5213", "metric_id": "838d0115b88b74cc76dd55a4a91e999f", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "5505a7d4730455e36bc064b81253151b", "metric_id": "a170d0ae00aa66b471c63c2199f94b89", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "a4cdbf848c55469c41f9df58d5c7c6e9", "metric_id": "af70180219d489ab90cdb4852e419dd4", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "3ca198cb44767b5c05c6987a539e202b", "metric_id": "99751793ea455891466fd8df194b2fa8", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "5adfde2497cbaf6e3686b02cd7eac332", "metric_id": "be72ef11f67f499392055912c04eaeb2", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "128e0f6e1aa3e8e40d07560247e29385", "metric_id": "6ecfcb8dd252f40bf7ab1b40b177fad0", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_percent value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 20.0, "average": 3.566666667, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "970010d052ccb9c383332d90b1592f20", "metric_id": "26c4c09af92a4b90ef62b3a2a0d7847b", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_percent", "anomaly_score": 5.294651389, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 20.0, "min_metric_value": -5.744616811, "max_metric_value": 12.877950144, "training_avg": 3.566666667, "training_stddev": 3.103761159, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_percent value is 20. The average for this metric is 3.567.", "is_anomalous": true}], "result_description": "In column MISSING_COUNT, the last null_percent value is 20. The average for this metric is 3.567."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "string_column_anomalies", "column_name": "AVERAGE_LENGTH", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:43:59+02:00", "latest_run_time_utc": "2023-01-02T10:43:59+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.elon_test.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "average_length", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average_length' as varchar)\n and upper(column_name) = upper(cast('AVERAGE_LENGTH' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column AVERAGE_LENGTH, the last average_length value is 6.44. The average for this metric is 5.048.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average_length' as varchar)\n and upper(column_name) = upper(cast('AVERAGE_LENGTH' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Average Length", "metrics": [{"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "ac716a424fcf58686f7f7bc47cc94047", "metric_id": "4ce8dc33a4f2464ae6b5a1d196cbef2a", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "43bc69336daf2e37bac3c2898c7fddb4", "metric_id": "e2ec54cc9408f6c2884ddbbe2bf4937e", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "b1838a3dd0a90c5b3629450f6c68dab8", "metric_id": "7987cd6726c3649ba4b77fa064a70853", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "a58815f7f6022c1ad4c956386eeedaff", "metric_id": "fd23a4f8d447c41a50d9edc8104f12ab", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "aa87df1e48fde545cf78da7af62f8f94", "metric_id": "71195f785cd5c3f0ba3f5e9630468323", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "9ec70e34806b316446833cd87c1259b5", "metric_id": "4c7d1541f6437c570beddb06c568e1bd", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "c2f86e3f524f0399d2ff3d88c67afe0e", "metric_id": "755cf41125041413bff2727da47379aa", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "0da8fbe443f8a29ce9d53fb31158786c", "metric_id": "33ca9cd1f1b249488214b18e466d235d", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "fdf7700c183921ce1a8b7252dac123d5", "metric_id": "ecb9aaca4342a96df30e564e427adde5", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "011f4c9783b8eb9ab9099c368def416c", "metric_id": "e81dea99a367a95c6673f6b2562bf6a8", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "482cb7b1fd732bb0bd80e43afe4b779a", "metric_id": "79e329374cbbcb518f243193e74f681a", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "22edc46d00802c0fa4bf38dbe11b8294", "metric_id": "7fb8122e5e95cb39d3578c658f3ec9d1", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "597b56a34ff83abd16b06594f8d66e39", "metric_id": "86927a6b469a51d967920aa14dac0902", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "bcc4b724e5c4c5fd955c6fcc94e5422d", "metric_id": "5be1a2eb4da94e7765311bdeb0bef08d", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "5cfdd694d15f2cfdf3776bac8a238364", "metric_id": "bab6175430dc3a94c61da678ac7c6ef7", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "c613ed1f60b09290dc26994da88f1d42", "metric_id": "f2f86c4e5d96e0f692625f73318908af", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "77125a8f2857fce79a0f2f044e319cb4", "metric_id": "ae0e6b02bb93fe2e78d7e9a85a2a4c99", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "6f6dabb123ed982bc1acafd9aa9a8df8", "metric_id": "1bc48738a23f77d1d8091689862cea89", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "b1abca9c4f81f9ce7b5d70e2cf3ae040", "metric_id": "d1cd6148618a85dd20f681ecf9448db5", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "4c5f838d314b1505113cfb7a4fb5760e", "metric_id": "42cf7ec673451a89031c5deb62af08c6", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "c8d9f7a9ea962da9ef236af97800bf41", "metric_id": "2add2779eae9eab254fa63f9f8a538dc", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "845c2a9e56e66ceeb62bdd80162320cc", "metric_id": "9e13d6270b2c18d5a04cb2d9aa714fab", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "baca7078a6d45fc8a2c2825f6a4a1c48", "metric_id": "605eea2689c30ba81ffc446a3c70e73a", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "957e90e51425e006367606d2cc08592e", "metric_id": "6888845a2dab101a9f99c667903959e8", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "2f173b0bbc99cbe09b2b63d35de60fe5", "metric_id": "6e7141236e5bac082045bf574cf0a672", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "e6477fc3e3312b71b94badcded2c17aa", "metric_id": "cc883fcb7eb7fcdfdb80d6a3342b4c2b", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "8ea4712a78896e4d4a64449a5131e2c5", "metric_id": "8546d7c7fb47fc5a4b16f44c2f73e296", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "2cae7c7151c1ae1e0cc56b82c81454ce", "metric_id": "ce5391f6169d9d033d5fca98d9215fed", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "average_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last average_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 6.44, "average": 5.048, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "d04c48c949725f6030b65119112427cd", "metric_id": "7ebc9acb411acaef738522a8b6c82f04", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "AVERAGE_LENGTH", "metric_name": "average_length", "anomaly_score": 5.294651389, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 6.44, "min_metric_value": 4.259279517, "max_metric_value": 5.836720483, "training_avg": 5.048, "training_stddev": 0.2629068276, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column AVERAGE_LENGTH, the last average_length value is 6.44. The average for this metric is 5.048.", "is_anomalous": true}], "result_description": "In column AVERAGE_LENGTH, the last average_length value is 6.44. The average for this metric is 5.048."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "string_column_anomalies", "column_name": "MISSING_COUNT", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:00+02:00", "latest_run_time_utc": "2023-01-02T10:44:00+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.elon_test.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "min_length", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_count__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('min_length' as varchar)\n and upper(column_name) = upper(cast('MISSING_COUNT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column MISSING_COUNT, the last min_length value is 5. The average for this metric is 5.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_count__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('min_length' as varchar)\n and upper(column_name) = upper(cast('MISSING_COUNT' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Min Length", "metrics": [{"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "cbeb0171a60f2b60e0f7365faa5539dc", "metric_id": "210985b429e03d6f3094116841008851", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "13ca71b314050256e6e1173b378a8b3a", "metric_id": "9980fc9f96d3da9f894ea36d115d684b", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "64fb1bc2f7266962f2926708506f0c85", "metric_id": "56c284270afb8e87bc295848895fae27", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "eefd98f4375067f2c9c3ee49a2efd958", "metric_id": "3bbe34622e8dd36f15097281870fb653", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "62791ef4d05a4591b9c2c6c651eaa2e9", "metric_id": "bd897f7a338b05c4d94b255c8b99a102", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "0c55a5300109d5a19a0a055a11af6f65", "metric_id": "7f592e7c11a127584996dfee579bab34", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "6940adfa76e3ce5a938f5c24f6a15c75", "metric_id": "bce904833983e28c57990521412db9e7", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "b68520217217863ec5135be14f2e15a5", "metric_id": "f87b338f3eb5a165708cf2adae2055fc", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "83497bc19ed406f97c65cf2ecb9f126e", "metric_id": "8626f096ef628823190e3648ac5c894b", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "cda15d8ecdbe9204549f81cc92874a1f", "metric_id": "4c1915811f37b8726b6df67811dec266", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "b2f29527225487acbda08509baa0338b", "metric_id": "5dac561570ccf15fbaf9fd14ba3bf073", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "05203ce12c87598df361b6b7859bf26d", "metric_id": "6febb5c403a35e42afc03c20f8a65e2e", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "6f13929baea99c17894fa9b87688b8df", "metric_id": "f1205871b9fd726c136e8746507917ef", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "e40fb84f86b0427f823feee1de14cb6d", "metric_id": "764484f61503e2099665293df3012229", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "d8c4ddfb099c002adf3d23ac627264af", "metric_id": "01ad75b1851f1d82575842df6e73df8a", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "2f8722ed15e3223b3bd65725cf77bd1b", "metric_id": "063268dde13585a256fad764fd6be0aa", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "d8482aa90fe65ba72f2f451c8b61456e", "metric_id": "801068bee9f6e294d120c0e9e1383a55", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "292368be099e34d19dc19925c4ffb049", "metric_id": "0a404f6ddd75ba836261f40eab725d97", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "1e463985ce3db9bf496c1a8a3996465f", "metric_id": "78438c24b14634c2594a139caf378991", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "a4679e96f581ac0c3bf86efbb56fcfb0", "metric_id": "edd796d052bfd800f4252537416d7674", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "861a11617e6b2c82aaa5580afcb053a0", "metric_id": "df733eea2f725dbf8864b0be94e4404e", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "b658b7f02e5f5aa038daec37705acab9", "metric_id": "d21ec897be604417900ba56d99cb3365", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "c5b47e0353a6ba38d932ed333f6143ce", "metric_id": "ccbab611b1800e467d725707ab1d8a77", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "9f0c6ddd4433f57e77d6b92f1ce96c61", "metric_id": "8321efcac71426bd8a0e86b11e94f7e4", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "df43667ee46606423d8347f87b0725cb", "metric_id": "6d472713931fa8e2ea2b0dd69fe0ad1e", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "39f1d9a6f423b8de7e944eaf976f92f0", "metric_id": "07965f7304ee01fd9a599352ac4c9331", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "377dce5154e1a2945d7403d36f17accc", "metric_id": "3a8fb29d3d43bc94726efae85d667053", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "453b0f528655330dbacd6b0d40346535", "metric_id": "c50c0a94b40a144e02d83ed49f1e2080", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "6aac6531690f6eb790fdba28aec1066e", "metric_id": "ed3be78f5232722e44002c93dbd3a420", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}], "result_description": "In column MISSING_COUNT, the last min_length value is 5. The average for this metric is 5."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "string_column_anomalies", "column_name": "MISSING_PERCENT", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:00+02:00", "latest_run_time_utc": "2023-01-02T10:44:00+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.elon_test.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "missing_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('missing_percent' as varchar)\n and upper(column_name) = upper(cast('MISSING_PERCENT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column MISSING_PERCENT, the last missing_percent value is 64. The average for this metric is 21.867.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('missing_percent' as varchar)\n and upper(column_name) = upper(cast('MISSING_PERCENT' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Missing Percent", "metrics": [{"value": 19.0, "average": 19.0, "min_value": 19.0, "max_value": 19.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "5699b2dd944c65caf7c59353b2e27c17", "metric_id": "cd427304568cdc55066bd391a4e41a74", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_percent", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 19.0, "min_metric_value": 19.0, "max_metric_value": 19.0, "training_avg": 19.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_percent value is 19. The average for this metric is 19.", "is_anomalous": false}, {"value": 21.0, "average": 19.666666667, "min_value": 16.202565052, "max_value": 23.130768282, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "93ab9a3deb2f202fee41f0dd9b0b282a", "metric_id": "fc4dcc68223850ff0772acef50322907", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_percent", "anomaly_score": 1.154700538, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 21.0, "min_metric_value": 16.202565052, "max_metric_value": 23.130768282, "training_avg": 19.666666667, "training_stddev": 1.154700538, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_percent value is 21. The average for this metric is 19.667.", "is_anomalous": false}, {"value": 17.0, "average": 19.0, "min_value": 14.101020514, "max_value": 23.898979486, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "0cf3ada09458f27deb794709106d9714", "metric_id": "afc32d9c8abdd71a8df19611f28b81db", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_percent", "anomaly_score": -1.224744871, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 17.0, "min_metric_value": 14.101020514, "max_metric_value": 23.898979486, "training_avg": 19.0, "training_stddev": 1.632993162, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_percent value is 17. The average for this metric is 19.", "is_anomalous": false}, {"value": 18.0, "average": 18.8, "min_value": 14.350280908, "max_value": 23.249719092, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "77a5ae96cfd660fe4d7955a64c142b4c", "metric_id": "2ab105f07eec047eedcae8e654279808", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_percent", "anomaly_score": -0.53935989, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 18.0, "min_metric_value": 14.350280908, "max_metric_value": 23.249719092, "training_avg": 18.8, "training_stddev": 1.483239697, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_percent value is 18. The average for this metric is 18.8.", "is_anomalous": false}, {"value": 29.0, "average": 20.5, "min_value": 7.38893597, "max_value": 33.61106403, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "e61b5d08019beb8a2d274302173cd6cb", "metric_id": "2967eccf866c92378de7d60a9b74a2cf", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_percent", "anomaly_score": 1.944922238, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 29.0, "min_metric_value": 7.38893597, "max_metric_value": 33.61106403, "training_avg": 20.5, "training_stddev": 4.370354677, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_percent value is 29. The average for this metric is 20.5.", "is_anomalous": false}, {"value": 23.0, "average": 20.857142857, "min_value": 8.557317074, "max_value": 33.15696864, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "59588bc38a8aaf106e5a8490f37872d3", "metric_id": "31f579015a222e182d2ca4e2a3944298", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_percent", "anomaly_score": 0.5226554865, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 23.0, "min_metric_value": 8.557317074, "max_metric_value": 33.15696864, "training_avg": 20.857142857, "training_stddev": 4.099941928, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_percent value is 23. The average for this metric is 20.857.", "is_anomalous": false}, {"value": 22.0, "average": 21.0, "min_value": 9.548237815, "max_value": 32.451762185, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "ca19572cb91f44c840efdb0fd41125e2", "metric_id": "b1f140bdc1cd90aec53ed9d165375587", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_percent", "anomaly_score": 0.261968416, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 22.0, "min_metric_value": 9.548237815, "max_metric_value": 32.451762185, "training_avg": 21.0, "training_stddev": 3.817254062, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_percent value is 22. The average for this metric is 21.", "is_anomalous": false}, {"value": 18.0, "average": 20.666666667, "min_value": 9.542368936, "max_value": 31.790964397, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "879e4183253037299413485042304de2", "metric_id": "6464d246611016c5e4b65690afc59aa1", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_percent", "anomaly_score": -0.71914652, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 18.0, "min_metric_value": 9.542368936, "max_metric_value": 31.790964397, "training_avg": 20.666666667, "training_stddev": 3.708099244, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_percent value is 18. The average for this metric is 20.667.", "is_anomalous": false}, {"value": 20.0, "average": 20.6, "min_value": 10.092859571, "max_value": 31.107140429, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "8fa7aa12f57819ed3c678546fa8cd9cb", "metric_id": "7836ca90141f5a566565fe91e181420c", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_percent", "anomaly_score": -0.1713120722, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 20.0, "min_metric_value": 10.092859571, "max_metric_value": 31.107140429, "training_avg": 20.6, "training_stddev": 3.502380143, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_percent value is 20. The average for this metric is 20.6.", "is_anomalous": false}, {"value": 23.0, "average": 20.818181818, "min_value": 10.616577666, "max_value": 31.01978597, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "87d4660b3870d50394a6bcedf5f03734", "metric_id": "785a7760c0af3c740ae56144877ba50f", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_percent", "anomaly_score": 0.641610324, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 23.0, "min_metric_value": 10.616577666, "max_metric_value": 31.01978597, "training_avg": 20.818181818, "training_stddev": 3.400534717, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_percent value is 23. The average for this metric is 20.818.", "is_anomalous": false}, {"value": 17.0, "average": 20.5, "min_value": 10.226468254, "max_value": 30.773531746, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "26805c8b02506e2defc4d665390b1d43", "metric_id": "ef71ccc9b41c481010dba4416708394a", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_percent", "anomaly_score": -1.022043856, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 17.0, "min_metric_value": 10.226468254, "max_metric_value": 30.773531746, "training_avg": 20.5, "training_stddev": 3.424510582, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_percent value is 17. The average for this metric is 20.5.", "is_anomalous": false}, {"value": 21.0, "average": 20.538461538, "min_value": 10.693509689, "max_value": 30.383413388, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "5548e977648cb7a9a1f631f5aa40dddf", "metric_id": "769ebd939968bba5700d23cf3df8c975", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_percent", "anomaly_score": 0.1406421693, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 21.0, "min_metric_value": 10.693509689, "max_metric_value": 30.383413388, "training_avg": 20.538461538, "training_stddev": 3.281650617, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_percent value is 21. The average for this metric is 20.538.", "is_anomalous": false}, {"value": 13.0, "average": 20.0, "min_value": 8.77502784, "max_value": 31.22497216, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "b922996309c319fabe4ac15af65fd408", "metric_id": "6e751e450a427a92ba1fbf6c37b35bff", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_percent", "anomaly_score": -1.870828693, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 13.0, "min_metric_value": 8.77502784, "max_metric_value": 31.22497216, "training_avg": 20.0, "training_stddev": 3.741657387, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_percent value is 13. The average for this metric is 20.", "is_anomalous": false}, {"value": 15.0, "average": 19.666666667, "min_value": 8.177541374, "max_value": 31.15579196, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "4e210d2cebc280f31823dfcc752ddf8b", "metric_id": "af2840bdc5771e1518c6c0f24bfe7f3a", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_percent", "anomaly_score": -1.218543592, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 15.0, "min_metric_value": 8.177541374, "max_metric_value": 31.15579196, "training_avg": 19.666666667, "training_stddev": 3.829708431, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_percent value is 15. The average for this metric is 19.667.", "is_anomalous": false}, {"value": 20.0, "average": 19.6875, "min_value": 8.585135387, "max_value": 30.789864613, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "d4695be8caea789cef8a620d26ee5ceb", "metric_id": "280ac9cbd2626e2abea96a5b5e2e8aa1", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_percent", "anomaly_score": 0.08444147105, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 20.0, "min_metric_value": 8.585135387, "max_metric_value": 30.789864613, "training_avg": 19.6875, "training_stddev": 3.700788204, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_percent value is 20. The average for this metric is 19.688.", "is_anomalous": false}, {"value": 26.0, "average": 20.058823529, "min_value": 8.368895738, "max_value": 31.748751321, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "2ab70a95ddbf4eed92994c2b30cd64bc", "metric_id": "2b2635141e3f803371d9e94064d1605f", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_percent", "anomaly_score": 1.524691147, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 26.0, "min_metric_value": 8.368895738, "max_metric_value": 31.748751321, "training_avg": 20.058823529, "training_stddev": 3.896642597, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_percent value is 26. The average for this metric is 20.059.", "is_anomalous": false}, {"value": 18.0, "average": 19.944444444, "min_value": 8.51049088, "max_value": 31.378398009, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "a10c9ccce8b3333202a3ff302e06e5e3", "metric_id": "e8ffacd2c1100c2e1a4f5972eef8f00f", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_percent", "anomaly_score": -0.5101764058, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 18.0, "min_metric_value": 8.51049088, "max_metric_value": 31.378398009, "training_avg": 19.944444444, "training_stddev": 3.811317855, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_percent value is 18. The average for this metric is 19.944.", "is_anomalous": false}, {"value": 26.0, "average": 20.263157895, "min_value": 8.395466238, "max_value": 32.130849552, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "c23eed617db4ab8c2c78157a78df3401", "metric_id": "1871949536ee7ea2a9578cf38d6f0c0f", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_percent", "anomaly_score": 1.450199989, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 26.0, "min_metric_value": 8.395466238, "max_metric_value": 32.130849552, "training_avg": 20.263157895, "training_stddev": 3.955897219, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_percent value is 26. The average for this metric is 20.263.", "is_anomalous": false}, {"value": 24.0, "average": 20.45, "min_value": 8.62996794, "max_value": 32.27003206, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "6472f9d89d5e68729ecca419a2af43ea", "metric_id": "30816baf08f1343c17fa752b1918de1f", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_percent", "anomaly_score": 0.9010127846, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 24.0, "min_metric_value": 8.62996794, "max_metric_value": 32.27003206, "training_avg": 20.45, "training_stddev": 3.940010687, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_percent value is 24. The average for this metric is 20.45.", "is_anomalous": false}, {"value": 26.0, "average": 20.714285714, "min_value": 8.634195838, "max_value": 32.794375591, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "a6c8f5a32bb1439dfa49932fa1d71cac", "metric_id": "3b6c99079785f7cfde563ae4a107d771", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_percent", "anomaly_score": 1.312667622, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 26.0, "min_metric_value": 8.634195838, "max_metric_value": 32.794375591, "training_avg": 20.714285714, "training_stddev": 4.026696626, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_percent value is 26. The average for this metric is 20.714.", "is_anomalous": false}, {"value": 19.0, "average": 20.636363636, "min_value": 8.796523343, "max_value": 32.47620393, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "a1d97150b25db70a4ec9cb42db253755", "metric_id": "09f90e29bff392387d98b34954e85d79", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_percent", "anomaly_score": -0.4146247574, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 19.0, "min_metric_value": 8.796523343, "max_metric_value": 32.47620393, "training_avg": 20.636363636, "training_stddev": 3.946613431, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_percent value is 19. The average for this metric is 20.636.", "is_anomalous": false}, {"value": 23.0, "average": 20.739130435, "min_value": 9.077396111, "max_value": 32.400864758, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "b4f22da2f05744ec505d407d7f1a6839", "metric_id": "bd43bd31f83eb695726f2babb0339a9d", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_percent", "anomaly_score": 0.5816123492, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 23.0, "min_metric_value": 9.077396111, "max_metric_value": 32.400864758, "training_avg": 20.739130435, "training_stddev": 3.887244774, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_percent value is 23. The average for this metric is 20.739.", "is_anomalous": false}, {"value": 18.0, "average": 20.625, "min_value": 9.096915201, "max_value": 32.153084799, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "c8ba7583add23044e9bbd085e4555c34", "metric_id": "4f6ffd6081e9fbc4d10be0a1b4a65e12", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_percent", "anomaly_score": -0.6831143366, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 18.0, "min_metric_value": 9.096915201, "max_metric_value": 32.153084799, "training_avg": 20.625, "training_stddev": 3.842694933, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_percent value is 18. The average for this metric is 20.625.", "is_anomalous": false}, {"value": 24.0, "average": 20.76, "min_value": 9.294399274, "max_value": 32.225600726, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "e4ee7c7b29b56b7058fbf0e5e0d1d4e5", "metric_id": "4344dea2f93e37e5d706277d63132eff", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_percent", "anomaly_score": 0.8477532257, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 24.0, "min_metric_value": 9.294399274, "max_metric_value": 32.225600726, "training_avg": 20.76, "training_stddev": 3.821866909, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_percent value is 24. The average for this metric is 20.76.", "is_anomalous": false}, {"value": 18.0, "average": 20.653846154, "min_value": 9.303143024, "max_value": 32.004549284, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "f316a8c6e153807d34f3ba7250f471e6", "metric_id": "993fbe96ae2f90ff3c57bbdc1a79bc8e", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_percent", "anomaly_score": -0.7014136808, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 18.0, "min_metric_value": 9.303143024, "max_metric_value": 32.004549284, "training_avg": 20.653846154, "training_stddev": 3.78356771, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_percent value is 18. The average for this metric is 20.654.", "is_anomalous": false}, {"value": 14.0, "average": 20.407407407, "min_value": 8.632815031, "max_value": 32.181999784, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "8fa46f3d8f826e8c6dc5f4725902728d", "metric_id": "290a816e6f0bfce73dda0b529a75024e", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_percent", "anomaly_score": -1.632517, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 14.0, "min_metric_value": 8.632815031, "max_metric_value": 32.181999784, "training_avg": 20.407407407, "training_stddev": 3.924864126, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_percent value is 14. The average for this metric is 20.407.", "is_anomalous": false}, {"value": 18.0, "average": 20.321428571, "min_value": 8.686608091, "max_value": 31.956249052, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "d10e9058e60a8c479440e0af6487cf30", "metric_id": "61ff8305dcd83154328a0257ba85931c", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_percent", "anomaly_score": -0.5985726832, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 18.0, "min_metric_value": 8.686608091, "max_metric_value": 31.956249052, "training_avg": 20.321428571, "training_stddev": 3.878273494, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_percent value is 18. The average for this metric is 20.321.", "is_anomalous": false}, {"value": 23.0, "average": 20.413793103, "min_value": 8.891593498, "max_value": 31.935992709, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "b349ecf8f2cdc87942541a11b60e575b", "metric_id": "dd92d0068c9860a53a43613bc5678560", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_percent", "anomaly_score": 0.6733628088, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 23.0, "min_metric_value": 8.891593498, "max_metric_value": 31.935992709, "training_avg": 20.413793103, "training_stddev": 3.840733202, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_percent value is 23. The average for this metric is 20.414.", "is_anomalous": false}, {"value": 64.0, "average": 21.866666667, "min_value": 8.891593498, "max_value": 31.935992709, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "d71514c36551666e6c9e3ebb274bb1df", "metric_id": "e5feb4114e0ce8d3cf79ed84e12d241a", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "missing_percent", "anomaly_score": 4.783932433, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 64.0, "min_metric_value": -4.555111185, "max_metric_value": 48.288444518, "training_avg": 21.866666667, "training_stddev": 8.807259284, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last missing_percent value is 64. The average for this metric is 21.867.", "is_anomalous": true}], "result_description": "In column MISSING_PERCENT, the last missing_percent value is 64. The average for this metric is 21.867."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "string_column_anomalies", "column_name": "MISSING_COUNT", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:00+02:00", "latest_run_time_utc": "2023-01-02T10:44:00+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.elon_test.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_count__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('MISSING_COUNT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column MISSING_COUNT, the last null_count value is 20. The average for this metric is 3.567.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_count__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('MISSING_COUNT' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Null Count", "metrics": [{"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "aab8eebfec62ec5be35f046d04b5e539", "metric_id": "334209b093e49897c034c73a7625ae1b", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "9ae0e6c9cb1c4b36fe48127b6f24bd49", "metric_id": "1ce267ef94c12cc43f43b1a395e11764", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "be481787e12c343194e18de23c9a2486", "metric_id": "e2c74bf18bfab6271935b8a26a725cb4", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "ce80cb27491ae3bda872e1666934312d", "metric_id": "8baf1daf53cca735261fc7f4be5805db", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "5f149143f9154365964e9e5e0d718dde", "metric_id": "e865648302cfd637ee980a3375d33a47", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "e6721b31d3c4b4aef9eceb8afc2abc92", "metric_id": "bb2f7fc4fe4321df4df00f2a162b4924", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "67d86196fcf1a511ae8b7111134f1161", "metric_id": "7d5112490b86250087e7a9938b1d5170", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "5dc8b5d4ca8c1558d2ce7ac030dcb73b", "metric_id": "5cc468b1697f066a32b90e152ea8fa19", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "3db4541b9cc3d4a6cd66b12daee383fc", "metric_id": "397b59b39d2d246c2686a47c6935e1f1", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "3aa5943eecdc7d5f7a0cbd13a610bd27", "metric_id": "c1332ebd78194f7b3cd7803210ff5fef", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "6c1f7101399ec8e79a0a295fb92e91d4", "metric_id": "86d867188f7125e9d6ef4f70c685d0e4", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "98bfd9823d5e2211d5c1f5d7b01dd1b0", "metric_id": "ee0fac2e1e96e5e28e148c55388129c0", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "0dfa067e469878c95bdcd438f10fdb21", "metric_id": "e661099a9e067b8dc9c467d7bc9e3734", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "8ffabad792278ce9ebfd1e1219a58845", "metric_id": "3569b4f8bdf3b8262debad2b36757115", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "7d15ed0d0ae356f2f58303d4099783e5", "metric_id": "277879ec92f2902bf365194a1c5350ae", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "16b963ef01b21e0082a8652f14aa3c6f", "metric_id": "2bd2eba2638cc2535d3d59667e10a69b", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "83015bc2641ec17a5c613df1c5d2058c", "metric_id": "298ddcd0bbd96357c68721e394f03ba0", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "e5c9a8284ba7fdfe62c556919542f9cd", "metric_id": "98575b63da350b7d521eaf2f32d2b646", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "6ff7e44206752aa1469f0025080e0d61", "metric_id": "58dd508b60055dbf894b09c6f3506324", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "95195503ab65986742d1e6caaff1d7f4", "metric_id": "8454d54bd571f9865083b4d474e5fb89", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "fb7803c5182838c764971baee23630eb", "metric_id": "aaf9f82fca3617bfe8c4ae4f14d8086f", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "04e23d53f46aac27dee55ff99436076b", "metric_id": "a6969695c1297a9c2673373a1061dc4d", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "148cbe3e7b575faa955cfd6056318d4f", "metric_id": "1b1070c629cdc76e20e0a97329623157", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "da316c206f3eececc9501193f804a5a0", "metric_id": "419806cd8afda7d5fdd2e59a5ba89820", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "620b65f5470c7079413652ea94612b2b", "metric_id": "0ea96e11213de92321cc27e0db33eec3", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "1e70569cafd4899efe5866c07d74acce", "metric_id": "499502aa42546446f66ec21482f88de1", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "9b57b69e6debafd8e8ee164aaf51df29", "metric_id": "178c8e6c4f896566c0f63774f094f51f", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 3.0, "average": 3.0, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "82c18a48f4c64cd609a346dbb2574d08", "metric_id": "dbb60d5a229f9e65330b1a800c7d7b9a", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 3.0, "min_metric_value": 3.0, "max_metric_value": 3.0, "training_avg": 3.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_count value is 3. The average for this metric is 3.", "is_anomalous": false}, {"value": 20.0, "average": 3.566666667, "min_value": 3.0, "max_value": 3.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "0ec0df4531dbcaec8d01c7c246126d93", "metric_id": "99c9360251cd609ade79cd3d31d4f103", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "detected_at": "2023-01-02T10:43:59.888000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_COUNT", "metric_name": "null_count", "anomaly_score": 5.294651389, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 20.0, "min_metric_value": -5.744616811, "max_metric_value": 12.877950144, "training_avg": 3.566666667, "training_stddev": 3.103761159, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_COUNT, the last null_count value is 20. The average for this metric is 3.567.", "is_anomalous": true}], "result_description": "In column MISSING_COUNT, the last null_count value is 20. The average for this metric is 3.567."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "string_column_anomalies", "column_name": "MISSING_PERCENT", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:00+02:00", "latest_run_time_utc": "2023-01-02T10:44:00+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.elon_test.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "max_length", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('max_length' as varchar)\n and upper(column_name) = upper(cast('MISSING_PERCENT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column MISSING_PERCENT, the last max_length value is 5. The average for this metric is 5.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('max_length' as varchar)\n and upper(column_name) = upper(cast('MISSING_PERCENT' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Max Length", "metrics": [{"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "f03291608a367c10adef1595585b4435", "metric_id": "229e7ccf0107403db5766565143c48bd", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "bdeac8c428c8b0443f1f02bf793252df", "metric_id": "a159a222b9ba93478af7b967993703bc", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "b2bfe744a89d1607fdcdeade60a15b46", "metric_id": "8a1832aa6e2c2fa7d3962c9847651e91", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "246ec43ca975ab0172f0b9e17ea732c9", "metric_id": "ca2ee6896e381448c9805bec6d0cc3a8", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "781d6a96a4d1d8e8a94192b69cd1ef9a", "metric_id": "05ee5679ab7391776d6ba713c8a2f536", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "35ffdb0aad839fbf2bc576ac760ea983", "metric_id": "6a2438cce77b7d09f83ba23ada7beda8", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "a237ae7647f523454bd708d0168f120e", "metric_id": "10150a57c4f271114b728320c64d2e8b", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "89aec5696a6c26cdaf5e08d74c3b045f", "metric_id": "b489a62e36129017e6c0be9cb505821d", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "4bd184537038183ce0d70a2b36d08a04", "metric_id": "e6acf7972300636dfd2abc5d2095376c", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "74ea5a8f1e0921be8bcba5ce9da884bd", "metric_id": "a824fb3a55f3321c2245deecf3304a03", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "1aebe96d67e58020e3bafcebea93fe76", "metric_id": "9253aeae20df10e7547dc2fab3561c68", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "4ad1039eb6450bddf45b84c0dac2537f", "metric_id": "c2568e40eb64bce5fe899d2fde09b186", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "f65da0a89909a8363389093d8e935a52", "metric_id": "f99a5decc01a831a03c3333f32831100", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "fffbee4119e0f847fe912309be368841", "metric_id": "f31eccf4467eca3a4e3926d37767f4e0", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "f1a5c784d7f24298c0bfce3b8c97042f", "metric_id": "c6f50e4504a1994074f6267b7971a41a", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "ee132d4d163733c7641593c1d550c3d2", "metric_id": "f073495d98d2938795d62363443e8c35", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "d9ddd54b2889d2caf4e6a53b902a0f6c", "metric_id": "efb2d85471d27331e597ec2456fa2e42", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "f403bebb0b51aafa65526aac06b05d83", "metric_id": "948278e71d4da4fc3682d07c5b115ebf", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "107b3608dbd4d47499e7fbf609e93b53", "metric_id": "df29f5e4e9f182f676dfd238ca2b99e2", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "17b3e2906a638e85ab1048d30ec22a96", "metric_id": "9ca8a19e386ac5a56bc38def41046ac3", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "b881c0d399a5919332628f00c1b502ea", "metric_id": "ca5508fbd12e1d6c56614c461a060996", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "bb6d46afe17371f9e079a26084828ab0", "metric_id": "b2cf82fdce5632b8e7ae1653fa7fc669", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "25163306dce1fb4552cd603c6919b14f", "metric_id": "bb0908311303bc7e5cd1832a7d17bce3", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "cc4a7eeb64e9d9143ac7f0738231fee1", "metric_id": "04f9c2ca8efbddf02a8d7305a6ba846f", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "1c490cc88ed2db3976d4445fb55a9f78", "metric_id": "02ab18d7fb4d04e85d2baa8c437f6720", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "b94d55d878d0401d0962ea92685a2be6", "metric_id": "84134ca4487c62c61cc8c2916b29977f", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "ba3d96996336eab645e29785bb0b775a", "metric_id": "a80db96f72cc117efa1a29ab6c1388ad", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "baf19d27bc3fa67abcc7ef6ea4c4cb47", "metric_id": "022080b2e811abba1c64f4549c812acf", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "dc6e778232230b258a69e6c7f65afa60", "metric_id": "f8b02cf48ab73c9a3a7b4f70d7cfb110", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "detected_at": "2023-01-02T10:43:59.811000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MISSING_PERCENT", "metric_name": "max_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MISSING_PERCENT, the last max_length value is 5. The average for this metric is 5.", "is_anomalous": false}], "result_description": "In column MISSING_PERCENT, the last max_length value is 5. The average for this metric is 5."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "string_column_anomalies", "column_name": "MIN_LENGTH", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:43:59+02:00", "latest_run_time_utc": "2023-01-02T10:43:59+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.elon_test.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "min_length", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('min_length' as varchar)\n and upper(column_name) = upper(cast('MIN_LENGTH' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column MIN_LENGTH, the last min_length value is 1. The average for this metric is 4.867.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('min_length' as varchar)\n and upper(column_name) = upper(cast('MIN_LENGTH' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Min Length", "metrics": [{"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "91a95e3318b13310187400cff711109c", "metric_id": "274ce9eea4f6574d36102765393de63d", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "a07772833fc94854f488f1a4b2d93127", "metric_id": "6e12c5a98e940d52f8e694d0f1227486", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "e4adac4fe761718872dbd38720df8413", "metric_id": "9eb53bcd9629acac8bb6a4d8dd45714c", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "d653de6011167a63790f100bf5e8335b", "metric_id": "bcadc48290e27d747ee720bbd6acd696", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "0c798bea846ce1ebd051a9bc556d37ce", "metric_id": "fb1a2c24958548d20cb291145b976491", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "7a76b1f9b48886eff7bf9c123682e164", "metric_id": "f77bcbf57b4ebedf49d3e9b4ea559459", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "6c9e10c2642c4db76d1bb4d8789b5888", "metric_id": "e82386f2e963f99fc875f436b81831a4", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "58e2faa91eff5fa87223cb8e12947e53", "metric_id": "d64adfcb3a737ba106f44ad16ed2a544", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "2d9b9d44431599f0a901959cfa8cddf3", "metric_id": "ebe216bcf090644b6e0ca668adeae393", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "a12e0bbb0caedd15eb9346ee0361789c", "metric_id": "bf4977e304ddf205eddb87b01d285ae3", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "f516ae2013c02de7d67c3840af3ccac7", "metric_id": "157be696281db22e56a2041dcf4e5694", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "fb1221d42c88880014915759c2f7f04d", "metric_id": "a2f4ea91c08eed58ff1f8bae4b74a729", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "16b2fe778ec7f4b3b4120d2569f8a8e8", "metric_id": "826db52d8b8da1878220b5526a700ab7", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "c97f9922671fbb04e47af6b2167ca313", "metric_id": "1b5bd8c0be65e038da8da036326c5429", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "82f0e9b43b2b3134ddee2a9688572972", "metric_id": "4958f87d9d12d76105be603eae8ce672", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "c1b97b422c278b5d737086451874ad3d", "metric_id": "fce5491f4297553bd7a8993bf5ddc29c", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "a6e531de0c96c0e636322936bd79a382", "metric_id": "de0ff0cecdcd5a9ba968f64ca86051b8", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "0efb54eaa3ffd5bab468891867602b3b", "metric_id": "194e1d28d54341c43d24a3573880e41a", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "40e0e3715bb29be6df66a72ea0cd7538", "metric_id": "59b14c3536c8e335893abf62c6f58ec7", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "eb025c2881af2d95b5000722fc91f270", "metric_id": "0e174b2691826b89a9c30b13159480df", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "d7fd119c6892b9559178145a54fa4fce", "metric_id": "b17fe0dfeb321185a107387ef6245ed8", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "015cd8a3038d0200ced4d117606ba8a5", "metric_id": "769c4101f26214f2025c3e435847cc68", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "c9d4704d197d28e8d72bd0aea8cd0412", "metric_id": "29561dfb10a45e9f8477d2b38583f476", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "bdb460ac17caebd2ec28d9cb005e7982", "metric_id": "a7e06b84d517efb4b7017dd4b4c8511b", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "71b88237af975e976bc08ccc2bf70e9f", "metric_id": "b8e3f5dadc401390c866753d61392dc2", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "7b102c9c92aea1428b8dc74a1ed8c303", "metric_id": "002ea1f8ac5b17c0737a3a89952d56f7", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "824be8f631203e64361c1222d01d3b31", "metric_id": "943efe27e643e1c2fbc20b2b8e87e531", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 5.0, "average": 5.0, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "120af791cca9618041f52ab5c04ca961", "metric_id": "979e58789af646039b89c9a3db15c2e1", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "min_length", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 5.0, "min_metric_value": 5.0, "max_metric_value": 5.0, "training_avg": 5.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last min_length value is 5. The average for this metric is 5.", "is_anomalous": false}, {"value": 1.0, "average": 4.866666667, "min_value": 5.0, "max_value": 5.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "22cb92fe040411e8208d8e220821ea23", "metric_id": "c64c19794937dafb4635062ccbac008c", "test_execution_id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "detected_at": "2023-01-02T10:43:58.289000", "full_table_name": "ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES", "column_name": "MIN_LENGTH", "metric_name": "min_length", "anomaly_score": -5.294651389, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 1.0, "min_metric_value": 2.675776437, "max_metric_value": 7.057556897, "training_avg": 4.866666667, "training_stddev": 0.7302967433, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "In column MIN_LENGTH, the last min_length value is 1. The average for this metric is 4.867.", "is_anomalous": true}], "result_description": "In column MIN_LENGTH, the last min_length value is 1. The average for this metric is 4.867."}}], "null": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.singular_test_with_two_refs", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": null, "column_name": null, "test_name": "singular_test_with_two_refs", "test_display_name": "Singular Test With Two Refs", "latest_run_time": "2023-01-02T12:46:31+02:00", "latest_run_time_utc": "2023-01-02T10:46:31+00:00", "latest_run_status": "fail", "model_unique_id": null, "table_unique_id": "elementary_tests.elon_test", "test_type": "dbt_test", "test_sub_type": "singular", "test_query": "with min_len_issues as (\n select null_count_int as min_issue from ELEMENTARY_TESTS.elon_test.any_type_column_anomalies where null_count_int < 100\n),\n\nmin_issues as (\n select min as min_issue from ELEMENTARY_TESTS.elon_test.numeric_column_anomalies where min < 100\n),\n\nall_issues as (\n select * from min_len_issues\n union all\n select * from min_issues\n)\n\nselect * from all_issues", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "Got 95 results, configured to fail if != 0", "result_query": "with min_len_issues as (\n select null_count_int as min_issue from ELEMENTARY_TESTS.elon_test.any_type_column_anomalies where null_count_int < 100\n),\n\nmin_issues as (\n select min as min_issue from ELEMENTARY_TESTS.elon_test.numeric_column_anomalies where min < 100\n),\n\nall_issues as (\n select * from min_len_issues\n union all\n select * from min_issues\n)\n\nselect * from all_issues"}, "configuration": {"test_name": "singular_test_with_two_refs", "test_params": null}}, "test_results": {"display_name": "singular_test_with_two_refs", "results_sample": [{"min_issue": 65.0}, {"min_issue": 97.0}, {"min_issue": 26.0}, {"min_issue": 88.0}, {"min_issue": 31.0}], "error_message": "Got 95 results, configured to fail if != 0", "failed_rows_count": 95}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.singular_test_with_no_ref", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": null, "column_name": null, "test_name": "singular_test_with_no_ref", "test_display_name": "Singular Test With No Ref", "latest_run_time": "2023-01-02T12:46:31+02:00", "latest_run_time_utc": "2023-01-02T10:46:31+00:00", "latest_run_status": "fail", "model_unique_id": null, "table_unique_id": "elementary_tests.elon_test", "test_type": "dbt_test", "test_sub_type": "singular", "test_query": "select min from ELEMENTARY_TESTS.elon_test.numeric_column_anomalies where min < 100", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "Got 95 results, configured to fail if != 0", "result_query": "select min from ELEMENTARY_TESTS.elon_test.numeric_column_anomalies where min < 100"}, "configuration": {"test_name": "singular_test_with_no_ref", "test_params": null}}, "test_results": {"display_name": "singular_test_with_no_ref", "results_sample": [{"min": 65.0}, {"min": 97.0}, {"min": 26.0}, {"min": 88.0}, {"min": 31.0}], "error_message": "Got 95 results, configured to fail if != 0", "failed_rows_count": 95}}], "source.elementary_integration_tests.training.string_column_anomalies_training": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "string_column_anomalies_training", "column_name": null, "test_name": "freshness_anomalies", "test_display_name": "Freshness Anomalies", "latest_run_time": "2023-01-02T12:42:19+02:00", "latest_run_time_utc": "2023-01-02T10:42:19+00:00", "latest_run_status": "fail", "model_unique_id": "source.elementary_integration_tests.training.string_column_anomalies_training", "table_unique_id": "elementary_tests.elon_test.string_column_anomalies_training", "test_type": "anomaly_detection", "test_sub_type": "freshness", "test_query": "select * from (\n \n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_SOURCE_FRESHNESS_ANOMALIES_TRAINING_STRING_COLUMN_ANOMALIES_TRAINING___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n\n \n\n\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING' as varchar)) and\n metric_name = cast('freshness' as varchar)", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "Last update was at 1969-12-30 00:00:00.000, 48 hours ago. Usually the table is updated within 24.8 hours.", "result_query": "select * from (\n \n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_SOURCE_FRESHNESS_ANOMALIES_TRAINING_STRING_COLUMN_ANOMALIES_TRAINING___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n\n \n\n\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING' as varchar)) and\n metric_name = cast('freshness' as varchar)"}, "configuration": {"test_name": "freshness_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Freshness", "metrics": [{"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "727d77d093c8b919b0325fc9d1e513e7", "metric_id": "3296d9cb9f1e41882d9d4ef4f0a71fd3", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-01-02T10:42:19.185000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-03 00:00:00.000", "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-03 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "889d3b50073e934b0c270380cb90ebe3", "metric_id": "b5ef35e848d7f856015420c33cdbdec2", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-01-02T10:42:19.185000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-04 00:00:00.000", "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-04 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "6d325966c139a93f77a44358247752cb", "metric_id": "b6644c13378118d58776ee5916b6242f", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-01-02T10:42:19.185000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-05 00:00:00.000", "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-05 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "a261cce97afe944353c30fb5a05eaf6d", "metric_id": "45761318fc670db1a1f49d51d81ca38f", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-01-02T10:42:19.185000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-06 00:00:00.000", "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-06 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "0d84a98324fb731018619d5131462ce8", "metric_id": "d2c22f0dea73ed898b9cc62a43c4692b", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-01-02T10:42:19.185000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-07 00:00:00.000", "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-07 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "15c41060b048ce85b33e2fb0befc82ed", "metric_id": "1fb4b5d08981ca9b9e085acd843c7671", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-01-02T10:42:19.185000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-08 00:00:00.000", "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-08 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "4bbb49814692cfeae7e250389cd60a96", "metric_id": "33cf398a864b266889a532d66229d01f", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-01-02T10:42:19.185000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-09 00:00:00.000", "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-09 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "9fa7c543d51950fb29f210d019d27bcb", "metric_id": "c3e0374c2433ac5aef331957dece2f96", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-01-02T10:42:19.185000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-10 00:00:00.000", "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-10 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "eba61f1980e84c4b778b7ea85093bf16", "metric_id": "a8a82b20f4785deb0ae5cd5e89981397", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-01-02T10:42:19.185000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-11 00:00:00.000", "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-11 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "8aeebc86685b7a8726b3111e32fe1f7a", "metric_id": "f8876963111b350d337bcb9b62e1e4a8", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-01-02T10:42:19.185000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-12 00:00:00.000", "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-12 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "ece7fa5f043c85595b8505419f8825ff", "metric_id": "78321463f36456ed53e59ad0bfc7c634", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-01-02T10:42:19.185000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-13 00:00:00.000", "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-13 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "c45d63ed1f9f610bfe3a0aae91c46a17", "metric_id": "a20da4864f1c9ff05ba14f4dd0b2e093", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-01-02T10:42:19.185000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-14 00:00:00.000", "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-14 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "21331d906069e869fa43828f7f3c4967", "metric_id": "ab840fea68256a1fbbdb6a9a8f62bf0a", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-01-02T10:42:19.185000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-15 00:00:00.000", "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-15 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "a097c3b08fbaf431960c48785329574a", "metric_id": "812b1ea307aac2747fde9beb3dc61099", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-01-02T10:42:19.185000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-16 00:00:00.000", "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-16 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "ff0b0a9fd0e1b0d0f147f01f546d36e9", "metric_id": "fb74620a35ed262d6a34af4538abe81c", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-01-02T10:42:19.185000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-17 00:00:00.000", "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-17 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "a62bb77598863f4f2dee257518edc9a6", "metric_id": "355e538fc4334136e4e063586c9e3576", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-01-02T10:42:19.185000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-18 00:00:00.000", "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-18 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "ff7be15eee3f44a59f119de6010e3fc7", "metric_id": "0d5265c251cbd3da509e1996abdcb5e6", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-01-02T10:42:19.185000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-19 00:00:00.000", "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-19 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "dfecef5662e52841edb8431ca0ccbeca", "metric_id": "603b7d28570057e17a92b9f2e00875cb", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-01-02T10:42:19.185000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-20 00:00:00.000", "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-20 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "adf9d34c6c5c15d467c59bdc84b0e02f", "metric_id": "e146458608a486ee77a762f8edea8e79", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-01-02T10:42:19.185000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-21 00:00:00.000", "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-21 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "3a030377b5f0df3e6f7a8271725b571a", "metric_id": "c3f89f6a3b74489a1d0668aee0d1fe07", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-01-02T10:42:19.185000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-22 00:00:00.000", "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-22 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "b6273982e2dfa371ce0eeeeb4a5f6d21", "metric_id": "9a94ffbe5842d855db59569810001030", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-01-02T10:42:19.185000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-23 00:00:00.000", "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-23 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "0d26563341ac1eb7564da3ad66230512", "metric_id": "ef3ffae192aca73f7039743930b01c30", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-01-02T10:42:19.185000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-24 00:00:00.000", "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-24 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "711388e6da604ae4e17672f8e1e9d2ad", "metric_id": "b8348d6b1ebf8f5255fab510b30e1df8", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-01-02T10:42:19.185000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-25 00:00:00.000", "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-25 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "b47da0172c6817853c930d2030a4c99d", "metric_id": "66b25666cc1831fac15c06c6e0843127", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-01-02T10:42:19.185000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-26 00:00:00.000", "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-26 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "485be335402b51013b270d9b540c69c0", "metric_id": "861e6c8b177ed1ac761a02a3406660f5", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-01-02T10:42:19.185000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-27 00:00:00.000", "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-27 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "195aa1dc9f72f802d0f64daee396351e", "metric_id": "aca1bfc56e9e5a6af69ea102bb6abb89", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-01-02T10:42:19.185000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-28 00:00:00.000", "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-28 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "eb1699379b4d305cf16283c45b85707a", "metric_id": "c3f17179d473b4e8969b3f15ff760021", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-01-02T10:42:19.185000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-29 00:00:00.000", "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-29 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "30c678a853fbf916f320eb6e0ac6ff83", "metric_id": "d5ed6d23dcf943747056afcfa69b3588", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-01-02T10:42:19.185000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-30 00:00:00.000", "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-30 00:00:00.000, 24 hours ago. Usually the table is updated within 24 hours.", "is_anomalous": false}, {"value": 172800.0, "average": 89280.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "f24d338eb577e8516bf88a6ce243f293", "metric_id": "08700a4324a520ccf4c2204e331f1dd0", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-01-02T10:42:19.185000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 5.294651389, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-30 00:00:00.000", "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 172800.0, "min_metric_value": 41956.771031554, "max_metric_value": 136603.228968446, "training_avg": 89280.0, "training_stddev": 15774.409656149, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-30 00:00:00.000, 48 hours ago. Usually the table is updated within 24.8 hours.", "is_anomalous": true}], "result_description": "Last update was at 1969-12-30 00:00:00.000, 48 hours ago. Usually the table is updated within 24.8 hours."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "string_column_anomalies_training", "column_name": null, "test_name": "volume_anomalies", "test_display_name": "Volume Anomalies", "latest_run_time": "2023-01-02T12:42:20+02:00", "latest_run_time_utc": "2023-01-02T10:42:20+00:00", "latest_run_status": "fail", "model_unique_id": "source.elementary_integration_tests.training.string_column_anomalies_training", "table_unique_id": "elementary_tests.elon_test.string_column_anomalies_training", "test_type": "anomaly_detection", "test_sub_type": "row_count", "test_query": "select * from (\n \n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_SOURCE_VOLUME_ANOMALIES_TRAINING_STRING_COLUMN_ANOMALIES_TRAINING___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n\n \n\n\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING' as varchar)) and\n metric_name = cast('row_count' as varchar)", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "The last row_count value is 0. The average for this metric is 96.667.", "result_query": "select * from (\n \n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_SOURCE_VOLUME_ANOMALIES_TRAINING_STRING_COLUMN_ANOMALIES_TRAINING___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n\n \n\n\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING' as varchar)) and\n metric_name = cast('row_count' as varchar)"}, "configuration": {"test_name": "volume_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Row Count", "metrics": [{"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "51299d376f33635da0a61377fa8d4b20", "metric_id": "7884121cd49dc56fa3dd17a1506ef60b", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-01-02T10:42:19.544000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "c6d9059069e08d07a7e4293380dff3c0", "metric_id": "1ec02241fabe051d8405b343c32720a6", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-01-02T10:42:19.544000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "5c3fb4ae4304f25c3a3698707aeb92a5", "metric_id": "80f074a183f29fe3e75be15fab5033bf", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-01-02T10:42:19.544000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "532c237fcfd18962bfffa4546203fb1e", "metric_id": "bd046b748b7b91239d146913e01e8a47", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-01-02T10:42:19.544000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "2bd6f5cfd2ab6746e151cfb7e62ac419", "metric_id": "82c8a77d0290a17965c7af5c7f98a969", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-01-02T10:42:19.544000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "f4e975a7f752d61885c2694b2cc73a28", "metric_id": "2e7478d7ef7ae5f1637713cef3fb2f53", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-01-02T10:42:19.544000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "b5e3f76eceb18e9f5789c0b7654df911", "metric_id": "9b3f5da26d79fcee6e28dcd3b6f4cb61", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-01-02T10:42:19.544000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "126f508616119b105c5f4ef4d98d8d77", "metric_id": "f21053f6c77a829d96d96ce23bb71a3a", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-01-02T10:42:19.544000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "f9414a0c570a33c138f7b4e4e7ef6091", "metric_id": "0dce107c69d03fa9243b1c53eb1755bb", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-01-02T10:42:19.544000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "ccc7f884d7d1e92032a04d3ad8ec3bc2", "metric_id": "2e72d13d570cc9c814eca6a01010ecb7", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-01-02T10:42:19.544000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "2bc314d5585575f96b5b732cb82a8f76", "metric_id": "2b6ef4450dc55a8ec1986aa0c379f57b", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-01-02T10:42:19.544000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "d489051feb8a1a3f629004cece6a4c11", "metric_id": "132c869b09e68085148d4262d1c9dc48", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-01-02T10:42:19.544000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "6d609a56b1ee0164a724420edb8c1474", "metric_id": "bb00b984e30ca67c91ca2f6a9809645e", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-01-02T10:42:19.544000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "1914a270da768a44f68e911ee56098ee", "metric_id": "c1aab65069d5ab7a24fe9c7f4e5b12b0", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-01-02T10:42:19.544000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "f48b0ddb42428f565b7431e643ca89ae", "metric_id": "0341203a0b4a4a6e683cf8a603db9d06", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-01-02T10:42:19.544000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "8860110289a5b228f975ee21dd8244b3", "metric_id": "09a0a664996282c8d719f34b1c195e32", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-01-02T10:42:19.544000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "5a5a038e1fef20f8a57c42d41bd050d6", "metric_id": "dc1a33f6bf6a39e58dcf565238c0d4c7", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-01-02T10:42:19.544000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "0bbf87e294479cb7e3a8c8d6e52fb88c", "metric_id": "b07cd474dd019d91433c4ad49efc6728", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-01-02T10:42:19.544000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "237a57d806db027a827696efbe899990", "metric_id": "e50a80c518b190e2aa7bd79152ae4e4a", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-01-02T10:42:19.544000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "f023577dbe5d82335bcfd9011c73ec9f", "metric_id": "9aed56521fde6a5459f03b15a3d9a17e", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-01-02T10:42:19.544000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "5db5eaae13c22c7a542c4188a67f620a", "metric_id": "1caf955021ab80ca3ed36397e464b433", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-01-02T10:42:19.544000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "8579ec4d34e4e84103661fab86729dd3", "metric_id": "9af1ea9a99ccb3ed04f821eafcb2a61a", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-01-02T10:42:19.544000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "37bd8e78f7aeed81c0b0bce60755e8e1", "metric_id": "3349c718b0d416a6d739df51fa58a400", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-01-02T10:42:19.544000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "90c60d6157432cc5f9d54251e1454eb2", "metric_id": "f3ae32e1cd4d41653c68789c783b2831", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-01-02T10:42:19.544000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "a482edb2d5f33f350cf65d972383e218", "metric_id": "860cd6cca05812182a9fab31b156f9df", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-01-02T10:42:19.544000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "8e755b960a6c2b5980fca2b5d4d1a4bc", "metric_id": "2c4935536357aee010b61c3af165739e", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-01-02T10:42:19.544000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "bec23c644a8619667684a373a3cd78ef", "metric_id": "6ae1c166400094aa3faf210372ba691c", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-01-02T10:42:19.544000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "e62284e00923c4b7c3e24fbda70120c8", "metric_id": "59c4cd3cc84e693abb9d6083be3660db", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-01-02T10:42:19.544000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100. The average for this metric is 100.", "is_anomalous": false}, {"value": 0.0, "average": 96.666666667, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "42e8c9d2ec5a53cee70bd6600874f216", "metric_id": "6dc4e28f466f21b43f64eac82971f9e5", "test_execution_id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-01-02T10:42:19.544000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": -5.294651389, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "metric_value": 0.0, "min_metric_value": 41.894410916, "max_metric_value": 151.438922417, "training_avg": 96.666666667, "training_stddev": 18.257418584, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 0. The average for this metric is 96.667.", "is_anomalous": true}], "result_description": "The last row_count value is 0. The average for this metric is 96.667."}}], "model.elementary_integration_tests.groups": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_schema_changes_from_baseline_groups_True.973df95f83", "database_name": "ELEMENTARY_TESTS", "schema_name": "ELON_TEST", "table_name": "GROUPS", "column_name": "group_d", "test_name": "schema_changes_from_baseline", "test_display_name": "Schema Changes From Baseline", "latest_run_time": "2023-01-02T12:43:23+02:00", "latest_run_time_utc": "2023-01-02T10:43:23+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.groups", "table_unique_id": "elementary_tests.elon_test.groups", "test_type": "schema_change", "test_sub_type": "column_added", "test_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n\n \n \n \n\n \n \n \n \n\n \n\n \n\n with cur as (\n \n with baseline as (\n select lower(column_name) as column_name, data_type\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_schema_changes_from_baseline_groups_true__schema_baseline__dbt_tmp\n )\n\n select\n info_schema.full_table_name,\n lower(info_schema.column_name) as column_name,\n info_schema.data_type,\n (baseline.column_name IS NULL) as is_new,\n \n cast ('2023-01-02 10:43:23' as TIMESTAMP)\n as detected_at\n from ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns info_schema\n left join baseline on (\n lower(info_schema.column_name) = lower(baseline.column_name)\n )\n where lower(info_schema.full_table_name) = lower('ELEMENTARY_TESTS.ELON_TEST.GROUPS')\n \n ),\n\n pre as (\n \n select\n cast('ELEMENTARY_TESTS.ELON_TEST.GROUPS' as varchar) as full_table_name,\n column_name,\n data_type,\n \n cast ('2023-01-02 10:43:23' as TIMESTAMP)\n as detected_at\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_schema_changes_from_baseline_groups_true__schema_baseline__dbt_tmp\n \n ),\n\n type_changes as (\n\n \n select\n cur.full_table_name,\n 'type_changed' as change,\n cur.column_name,\n cur.data_type as data_type,\n pre.data_type as pre_data_type,\n pre.detected_at\n from cur inner join pre\n on (cur.full_table_name = pre.full_table_name and cur.column_name = pre.column_name)\n where pre.data_type IS NOT NULL AND cur.data_type != pre.data_type\n\n ),\n\n \n columns_added as (\n\n \n select\n full_table_name,\n 'column_added' as change,\n column_name,\n data_type,\n \n cast(null as varchar)\n as pre_data_type,\n detected_at as detected_at\n from cur\n where is_new = true\n\n ),\n \n\n columns_removed as (\n\n \n select\n pre.full_table_name,\n 'column_removed' as change,\n pre.column_name as column_name,\n \n cast(null as varchar)\n as data_type,\n pre.data_type as pre_data_type,\n pre.detected_at as detected_at\n from pre left join cur\n on (cur.full_table_name = pre.full_table_name and cur.column_name = pre.column_name)\n where cur.full_table_name is null and cur.column_name is null\n\n ),\n\n columns_removed_filter_deleted_tables as (\n\n \n select\n removed.full_table_name,\n removed.change,\n removed.column_name,\n removed.data_type,\n removed.pre_data_type,\n removed.detected_at\n from columns_removed as removed join cur\n on (removed.full_table_name = cur.full_table_name)\n\n ),\n\n all_column_changes as (\n\n \n select * from type_changes\n union all\n select * from columns_removed_filter_deleted_tables\n \n union all\n select * from columns_added\n \n ),\n\n column_changes_test_results as (\n\n \n select\n \n \n\n md5(cast(coalesce(cast(full_table_name as varchar), '') || '-' || coalesce(cast(column_name as varchar), '') || '-' || coalesce(cast(change as varchar), '') || '-' || coalesce(cast(detected_at as varchar), '') as TEXT))\n as data_issue_id,\n \n cast ('2023-01-02 10:43:23' as TIMESTAMP)\n as detected_at,\n \n trim(split(full_table_name,'.')[0],'\"') as database_name\n\n,\n \n trim(split(full_table_name,'.')[1],'\"') as schema_name\n\n,\n \n trim(split(full_table_name,'.')[2],'\"') as table_name\n\n,\n column_name,\n 'schema_change' as test_type,\n change as test_sub_type,\n case\n when change = 'column_added'\n then 'The column \"' || column_name || '\" was added'\n when change= 'column_removed'\n then 'The column \"' || column_name || '\" was removed'\n when change= 'type_changed'\n then 'The type of \"' || column_name || '\" was changed from ' || pre_data_type || ' to ' || data_type\n else NULL\n end as test_results_description\n from all_column_changes\n group by 1,2,3,4,5,6,7,8,9\n\n )\n\n \n select \n \n\n md5(cast(coalesce(cast(data_issue_id as varchar), '') || '-' || coalesce(cast(cast('acd0caa6-5efb-4df9-b512-b449e218426f.test.elementary_integration_tests.elementary_schema_changes_from_baseline_groups_True.973df95f83' as varchar) as varchar), '') as TEXT))\n as id,\n cast('acd0caa6-5efb-4df9-b512-b449e218426f.test.elementary_integration_tests.elementary_schema_changes_from_baseline_groups_True.973df95f83' as varchar) as test_execution_id,\n cast('test.elementary_integration_tests.elementary_schema_changes_from_baseline_groups_True.973df95f83' as varchar) as test_unique_id,\n *\n from column_changes_test_results", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "The column \"group_d\" was added", "result_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n\n \n \n \n\n \n \n \n \n\n \n\n \n\n with cur as (\n \n with baseline as (\n select lower(column_name) as column_name, data_type\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_schema_changes_from_baseline_groups_true__schema_baseline__dbt_tmp\n )\n\n select\n info_schema.full_table_name,\n lower(info_schema.column_name) as column_name,\n info_schema.data_type,\n (baseline.column_name IS NULL) as is_new,\n \n cast ('2023-01-02 10:43:23' as TIMESTAMP)\n as detected_at\n from ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns info_schema\n left join baseline on (\n lower(info_schema.column_name) = lower(baseline.column_name)\n )\n where lower(info_schema.full_table_name) = lower('ELEMENTARY_TESTS.ELON_TEST.GROUPS')\n \n ),\n\n pre as (\n \n select\n cast('ELEMENTARY_TESTS.ELON_TEST.GROUPS' as varchar) as full_table_name,\n column_name,\n data_type,\n \n cast ('2023-01-02 10:43:23' as TIMESTAMP)\n as detected_at\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_schema_changes_from_baseline_groups_true__schema_baseline__dbt_tmp\n \n ),\n\n type_changes as (\n\n \n select\n cur.full_table_name,\n 'type_changed' as change,\n cur.column_name,\n cur.data_type as data_type,\n pre.data_type as pre_data_type,\n pre.detected_at\n from cur inner join pre\n on (cur.full_table_name = pre.full_table_name and cur.column_name = pre.column_name)\n where pre.data_type IS NOT NULL AND cur.data_type != pre.data_type\n\n ),\n\n \n columns_added as (\n\n \n select\n full_table_name,\n 'column_added' as change,\n column_name,\n data_type,\n \n cast(null as varchar)\n as pre_data_type,\n detected_at as detected_at\n from cur\n where is_new = true\n\n ),\n \n\n columns_removed as (\n\n \n select\n pre.full_table_name,\n 'column_removed' as change,\n pre.column_name as column_name,\n \n cast(null as varchar)\n as data_type,\n pre.data_type as pre_data_type,\n pre.detected_at as detected_at\n from pre left join cur\n on (cur.full_table_name = pre.full_table_name and cur.column_name = pre.column_name)\n where cur.full_table_name is null and cur.column_name is null\n\n ),\n\n columns_removed_filter_deleted_tables as (\n\n \n select\n removed.full_table_name,\n removed.change,\n removed.column_name,\n removed.data_type,\n removed.pre_data_type,\n removed.detected_at\n from columns_removed as removed join cur\n on (removed.full_table_name = cur.full_table_name)\n\n ),\n\n all_column_changes as (\n\n \n select * from type_changes\n union all\n select * from columns_removed_filter_deleted_tables\n \n union all\n select * from columns_added\n \n ),\n\n column_changes_test_results as (\n\n \n select\n \n \n\n md5(cast(coalesce(cast(full_table_name as varchar), '') || '-' || coalesce(cast(column_name as varchar), '') || '-' || coalesce(cast(change as varchar), '') || '-' || coalesce(cast(detected_at as varchar), '') as TEXT))\n as data_issue_id,\n \n cast ('2023-01-02 10:43:23' as TIMESTAMP)\n as detected_at,\n \n trim(split(full_table_name,'.')[0],'\"') as database_name\n\n,\n \n trim(split(full_table_name,'.')[1],'\"') as schema_name\n\n,\n \n trim(split(full_table_name,'.')[2],'\"') as table_name\n\n,\n column_name,\n 'schema_change' as test_type,\n change as test_sub_type,\n case\n when change = 'column_added'\n then 'The column \"' || column_name || '\" was added'\n when change= 'column_removed'\n then 'The column \"' || column_name || '\" was removed'\n when change= 'type_changed'\n then 'The type of \"' || column_name || '\" was changed from ' || pre_data_type || ' to ' || data_type\n else NULL\n end as test_results_description\n from all_column_changes\n group by 1,2,3,4,5,6,7,8,9\n\n )\n\n \n select \n \n\n md5(cast(coalesce(cast(data_issue_id as varchar), '') || '-' || coalesce(cast(cast('acd0caa6-5efb-4df9-b512-b449e218426f.test.elementary_integration_tests.elementary_schema_changes_from_baseline_groups_True.973df95f83' as varchar) as varchar), '') as TEXT))\n as id,\n cast('acd0caa6-5efb-4df9-b512-b449e218426f.test.elementary_integration_tests.elementary_schema_changes_from_baseline_groups_True.973df95f83' as varchar) as test_execution_id,\n cast('test.elementary_integration_tests.elementary_schema_changes_from_baseline_groups_True.973df95f83' as varchar) as test_unique_id,\n *\n from column_changes_test_results"}, "configuration": {"test_name": "schema_changes_from_baseline", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "column added", "metrics": null, "result_description": "The column \"group_d\" was added"}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_schema_changes_groups_.b321d3c7ba", "database_name": "ELEMENTARY_TESTS", "schema_name": "ELON_TEST", "table_name": "GROUPS", "column_name": "GROUP_A", "test_name": "schema_changes", "test_display_name": "Schema Changes", "latest_run_time": "2023-01-02T12:46:13+02:00", "latest_run_time_utc": "2023-01-02T10:46:13+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.groups", "table_unique_id": "elementary_tests.elon_test.groups", "test_type": "schema_change", "test_sub_type": "column_removed", "test_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.elementary_test_results\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n \n\n \n \n \n\n \n \n \n\n \n \n \n\n \n\n \n \n \n \n select * from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_SCHEMA_CHANGES_GROUPS___SCHEMA_CHANGES_ALERTS\"", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "The column \"GROUP_A\" was removed", "result_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.elementary_test_results\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n \n\n \n \n \n\n \n \n \n\n \n \n \n\n \n\n \n \n \n \n select * from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_SCHEMA_CHANGES_GROUPS___SCHEMA_CHANGES_ALERTS\""}, "configuration": {"test_name": "schema_changes", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "column removed", "metrics": null, "result_description": "The column \"GROUP_A\" was removed"}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_schema_changes_from_baseline_groups_True.973df95f83", "database_name": "ELEMENTARY_TESTS", "schema_name": "ELON_TEST", "table_name": "GROUPS", "column_name": "group_b", "test_name": "schema_changes_from_baseline", "test_display_name": "Schema Changes From Baseline", "latest_run_time": "2023-01-02T12:43:23+02:00", "latest_run_time_utc": "2023-01-02T10:43:23+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.groups", "table_unique_id": "elementary_tests.elon_test.groups", "test_type": "schema_change", "test_sub_type": "type_changed", "test_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n\n \n \n \n\n \n \n \n \n\n \n\n \n\n with cur as (\n \n with baseline as (\n select lower(column_name) as column_name, data_type\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_schema_changes_from_baseline_groups_true__schema_baseline__dbt_tmp\n )\n\n select\n info_schema.full_table_name,\n lower(info_schema.column_name) as column_name,\n info_schema.data_type,\n (baseline.column_name IS NULL) as is_new,\n \n cast ('2023-01-02 10:43:23' as TIMESTAMP)\n as detected_at\n from ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns info_schema\n left join baseline on (\n lower(info_schema.column_name) = lower(baseline.column_name)\n )\n where lower(info_schema.full_table_name) = lower('ELEMENTARY_TESTS.ELON_TEST.GROUPS')\n \n ),\n\n pre as (\n \n select\n cast('ELEMENTARY_TESTS.ELON_TEST.GROUPS' as varchar) as full_table_name,\n column_name,\n data_type,\n \n cast ('2023-01-02 10:43:23' as TIMESTAMP)\n as detected_at\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_schema_changes_from_baseline_groups_true__schema_baseline__dbt_tmp\n \n ),\n\n type_changes as (\n\n \n select\n cur.full_table_name,\n 'type_changed' as change,\n cur.column_name,\n cur.data_type as data_type,\n pre.data_type as pre_data_type,\n pre.detected_at\n from cur inner join pre\n on (cur.full_table_name = pre.full_table_name and cur.column_name = pre.column_name)\n where pre.data_type IS NOT NULL AND cur.data_type != pre.data_type\n\n ),\n\n \n columns_added as (\n\n \n select\n full_table_name,\n 'column_added' as change,\n column_name,\n data_type,\n \n cast(null as varchar)\n as pre_data_type,\n detected_at as detected_at\n from cur\n where is_new = true\n\n ),\n \n\n columns_removed as (\n\n \n select\n pre.full_table_name,\n 'column_removed' as change,\n pre.column_name as column_name,\n \n cast(null as varchar)\n as data_type,\n pre.data_type as pre_data_type,\n pre.detected_at as detected_at\n from pre left join cur\n on (cur.full_table_name = pre.full_table_name and cur.column_name = pre.column_name)\n where cur.full_table_name is null and cur.column_name is null\n\n ),\n\n columns_removed_filter_deleted_tables as (\n\n \n select\n removed.full_table_name,\n removed.change,\n removed.column_name,\n removed.data_type,\n removed.pre_data_type,\n removed.detected_at\n from columns_removed as removed join cur\n on (removed.full_table_name = cur.full_table_name)\n\n ),\n\n all_column_changes as (\n\n \n select * from type_changes\n union all\n select * from columns_removed_filter_deleted_tables\n \n union all\n select * from columns_added\n \n ),\n\n column_changes_test_results as (\n\n \n select\n \n \n\n md5(cast(coalesce(cast(full_table_name as varchar), '') || '-' || coalesce(cast(column_name as varchar), '') || '-' || coalesce(cast(change as varchar), '') || '-' || coalesce(cast(detected_at as varchar), '') as TEXT))\n as data_issue_id,\n \n cast ('2023-01-02 10:43:23' as TIMESTAMP)\n as detected_at,\n \n trim(split(full_table_name,'.')[0],'\"') as database_name\n\n,\n \n trim(split(full_table_name,'.')[1],'\"') as schema_name\n\n,\n \n trim(split(full_table_name,'.')[2],'\"') as table_name\n\n,\n column_name,\n 'schema_change' as test_type,\n change as test_sub_type,\n case\n when change = 'column_added'\n then 'The column \"' || column_name || '\" was added'\n when change= 'column_removed'\n then 'The column \"' || column_name || '\" was removed'\n when change= 'type_changed'\n then 'The type of \"' || column_name || '\" was changed from ' || pre_data_type || ' to ' || data_type\n else NULL\n end as test_results_description\n from all_column_changes\n group by 1,2,3,4,5,6,7,8,9\n\n )\n\n \n select \n \n\n md5(cast(coalesce(cast(data_issue_id as varchar), '') || '-' || coalesce(cast(cast('acd0caa6-5efb-4df9-b512-b449e218426f.test.elementary_integration_tests.elementary_schema_changes_from_baseline_groups_True.973df95f83' as varchar) as varchar), '') as TEXT))\n as id,\n cast('acd0caa6-5efb-4df9-b512-b449e218426f.test.elementary_integration_tests.elementary_schema_changes_from_baseline_groups_True.973df95f83' as varchar) as test_execution_id,\n cast('test.elementary_integration_tests.elementary_schema_changes_from_baseline_groups_True.973df95f83' as varchar) as test_unique_id,\n *\n from column_changes_test_results", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "The type of \"group_b\" was changed from DOUBLE to TEXT", "result_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n\n \n \n \n\n \n \n \n \n\n \n\n \n\n with cur as (\n \n with baseline as (\n select lower(column_name) as column_name, data_type\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_schema_changes_from_baseline_groups_true__schema_baseline__dbt_tmp\n )\n\n select\n info_schema.full_table_name,\n lower(info_schema.column_name) as column_name,\n info_schema.data_type,\n (baseline.column_name IS NULL) as is_new,\n \n cast ('2023-01-02 10:43:23' as TIMESTAMP)\n as detected_at\n from ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns info_schema\n left join baseline on (\n lower(info_schema.column_name) = lower(baseline.column_name)\n )\n where lower(info_schema.full_table_name) = lower('ELEMENTARY_TESTS.ELON_TEST.GROUPS')\n \n ),\n\n pre as (\n \n select\n cast('ELEMENTARY_TESTS.ELON_TEST.GROUPS' as varchar) as full_table_name,\n column_name,\n data_type,\n \n cast ('2023-01-02 10:43:23' as TIMESTAMP)\n as detected_at\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_schema_changes_from_baseline_groups_true__schema_baseline__dbt_tmp\n \n ),\n\n type_changes as (\n\n \n select\n cur.full_table_name,\n 'type_changed' as change,\n cur.column_name,\n cur.data_type as data_type,\n pre.data_type as pre_data_type,\n pre.detected_at\n from cur inner join pre\n on (cur.full_table_name = pre.full_table_name and cur.column_name = pre.column_name)\n where pre.data_type IS NOT NULL AND cur.data_type != pre.data_type\n\n ),\n\n \n columns_added as (\n\n \n select\n full_table_name,\n 'column_added' as change,\n column_name,\n data_type,\n \n cast(null as varchar)\n as pre_data_type,\n detected_at as detected_at\n from cur\n where is_new = true\n\n ),\n \n\n columns_removed as (\n\n \n select\n pre.full_table_name,\n 'column_removed' as change,\n pre.column_name as column_name,\n \n cast(null as varchar)\n as data_type,\n pre.data_type as pre_data_type,\n pre.detected_at as detected_at\n from pre left join cur\n on (cur.full_table_name = pre.full_table_name and cur.column_name = pre.column_name)\n where cur.full_table_name is null and cur.column_name is null\n\n ),\n\n columns_removed_filter_deleted_tables as (\n\n \n select\n removed.full_table_name,\n removed.change,\n removed.column_name,\n removed.data_type,\n removed.pre_data_type,\n removed.detected_at\n from columns_removed as removed join cur\n on (removed.full_table_name = cur.full_table_name)\n\n ),\n\n all_column_changes as (\n\n \n select * from type_changes\n union all\n select * from columns_removed_filter_deleted_tables\n \n union all\n select * from columns_added\n \n ),\n\n column_changes_test_results as (\n\n \n select\n \n \n\n md5(cast(coalesce(cast(full_table_name as varchar), '') || '-' || coalesce(cast(column_name as varchar), '') || '-' || coalesce(cast(change as varchar), '') || '-' || coalesce(cast(detected_at as varchar), '') as TEXT))\n as data_issue_id,\n \n cast ('2023-01-02 10:43:23' as TIMESTAMP)\n as detected_at,\n \n trim(split(full_table_name,'.')[0],'\"') as database_name\n\n,\n \n trim(split(full_table_name,'.')[1],'\"') as schema_name\n\n,\n \n trim(split(full_table_name,'.')[2],'\"') as table_name\n\n,\n column_name,\n 'schema_change' as test_type,\n change as test_sub_type,\n case\n when change = 'column_added'\n then 'The column \"' || column_name || '\" was added'\n when change= 'column_removed'\n then 'The column \"' || column_name || '\" was removed'\n when change= 'type_changed'\n then 'The type of \"' || column_name || '\" was changed from ' || pre_data_type || ' to ' || data_type\n else NULL\n end as test_results_description\n from all_column_changes\n group by 1,2,3,4,5,6,7,8,9\n\n )\n\n \n select \n \n\n md5(cast(coalesce(cast(data_issue_id as varchar), '') || '-' || coalesce(cast(cast('acd0caa6-5efb-4df9-b512-b449e218426f.test.elementary_integration_tests.elementary_schema_changes_from_baseline_groups_True.973df95f83' as varchar) as varchar), '') as TEXT))\n as id,\n cast('acd0caa6-5efb-4df9-b512-b449e218426f.test.elementary_integration_tests.elementary_schema_changes_from_baseline_groups_True.973df95f83' as varchar) as test_execution_id,\n cast('test.elementary_integration_tests.elementary_schema_changes_from_baseline_groups_True.973df95f83' as varchar) as test_unique_id,\n *\n from column_changes_test_results"}, "configuration": {"test_name": "schema_changes_from_baseline", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "type changed", "metrics": null, "result_description": "The type of \"group_b\" was changed from DOUBLE to TEXT"}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_schema_changes_groups_.b321d3c7ba", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "groups", "column_name": null, "test_name": "schema_changes", "test_display_name": "Schema Changes", "latest_run_time": "2023-01-02T12:43:14+02:00", "latest_run_time_utc": "2023-01-02T10:43:14+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.groups", "table_unique_id": "elementary_tests.elon_test.groups", "test_type": "schema_change", "test_sub_type": "generic", "test_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.elementary_test_results\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n \n\n \n \n \n\n \n \n \n\n \n \n \n\n \n\n \n \n \n \n select * from ELEMENTARY_TESTS.elon_test_elementary.elementary_schema_changes_groups___schema_changes_alerts", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.elementary_test_results\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n \n\n \n \n \n\n \n \n \n\n \n \n \n\n \n\n \n \n \n \n select * from ELEMENTARY_TESTS.elon_test_elementary.elementary_schema_changes_groups___schema_changes_alerts"}, "configuration": {"test_name": "schema_changes", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "generic", "metrics": null, "result_description": null}}], "model.elementary_integration_tests.error_model": [{"metadata": {"test_unique_id": "model.elementary_integration_tests.error_model.missing_column.uniques_error_model_missing_column.uniques", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "error_model", "column_name": "missing_column", "test_name": "uniques", "test_display_name": "Uniques", "latest_run_time": "2023-01-02T12:46:34+02:00", "latest_run_time_utc": "2023-01-02T10:46:34+00:00", "latest_run_status": "error", "model_unique_id": "model.elementary_integration_tests.error_model", "table_unique_id": "elementary_tests.elon_test.error_model", "test_type": "dbt_test", "test_sub_type": "generic", "test_query": null, "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "Compilation Error in test uniques_error_model_missing_column (models/schema.yml)\n 'test_uniques' is undefined. This can happen when calling a macro that does not exist. Check for typos and/or install package dependencies with \"dbt deps\".", "result_query": null}, "configuration": {"test_name": "uniques", "test_params": null}}, "test_results": {"display_name": "uniques", "results_sample": [], "error_message": "Compilation Error in test uniques_error_model_missing_column (models/schema.yml)\n 'test_uniques' is undefined. This can happen when calling a macro that does not exist. Check for typos and/or install package dependencies with \"dbt deps\".", "failed_rows_count": -1}}], "model.elementary_integration_tests.dimension_anomalies": [{"metadata": {"test_unique_id": "model.elementary_integration_tests.dimension_anomalies.elementary_dimension_anomalies_dimension_anomalies_platform.dimension_anomalies", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "dimension_anomalies", "column_name": null, "test_name": "dimension_anomalies", "test_display_name": "Dimension Anomalies", "latest_run_time": "2023-01-02T12:45:53+02:00", "latest_run_time_utc": "2023-01-02T10:45:53+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.dimension_anomalies", "table_unique_id": "elementary_tests.elon_test.dimension_anomalies", "test_type": "anomaly_detection", "test_sub_type": "dimension", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n\n \n \n \n \n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n select * from (with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_dimension_anomalies_dimension_anomalies_platform__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value)\n where is_anomalous = true\n\n \n\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.DIMENSION_ANOMALIES' as varchar)) and\n metric_name = cast('dimension' as varchar)", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "The last dimension value for dimension platform - ios is 1. The average for this metric is 19.367.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n\n \n \n \n \n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n select * from (with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_dimension_anomalies_dimension_anomalies_platform__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value)\n where is_anomalous = true\n\n \n\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.DIMENSION_ANOMALIES' as varchar)) and\n metric_name = cast('dimension' as varchar)"}, "configuration": {"test_name": "dimension_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Dimension", "metrics": {"headers": [{"id": "anomalous_value_timestamp", "display_name": "timestamp", "type": "date"}, {"id": "platform", "display_name": "platform", "type": "str"}, {"id": "anomalous_value_row_count", "display_name": "row count", "type": "int"}, {"id": "anomalous_value_average_row_count", "display_name": "average row count", "type": "int"}], "test_rows_sample": [{"anomalous_value_timestamp": "1970-01-01T00:00:00", "anomalous_value_row_count": 1.0, "anomalous_value_average_row_count": 19.4, "platform": "ios"}]}, "result_description": "The last dimension value for dimension platform - ios is 1. The average for this metric is 19.367."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_dimension_anomalies_dimension_anomalies_platform__version.b65d46fbf9", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "dimension_anomalies", "column_name": null, "test_name": "dimension_anomalies", "test_display_name": "Dimension Anomalies", "latest_run_time": "2023-01-02T12:45:53+02:00", "latest_run_time_utc": "2023-01-02T10:45:53+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.dimension_anomalies", "table_unique_id": "elementary_tests.elon_test.dimension_anomalies", "test_type": "anomaly_detection", "test_sub_type": "dimension", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n\n \n \n \n \n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n select * from (with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_dimension_anomalies_dimension_anomalies_platform__version__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value)\n where is_anomalous = true\n\n \n\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.DIMENSION_ANOMALIES' as varchar)) and\n metric_name = cast('dimension' as varchar)", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "The last dimension value for dimension platform; version - ios; 2 is 0. The average for this metric is 6.767.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n\n \n \n \n \n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n select * from (with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_dimension_anomalies_dimension_anomalies_platform__version__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value)\n where is_anomalous = true\n\n \n\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.DIMENSION_ANOMALIES' as varchar)) and\n metric_name = cast('dimension' as varchar)"}, "configuration": {"test_name": "dimension_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Dimension", "metrics": {"headers": [{"id": "anomalous_value_timestamp", "display_name": "timestamp", "type": "date"}, {"id": "platform", "display_name": "platform", "type": "str"}, {"id": "version", "display_name": "version", "type": "str"}, {"id": "anomalous_value_row_count", "display_name": "row count", "type": "int"}, {"id": "anomalous_value_average_row_count", "display_name": "average row count", "type": "int"}], "test_rows_sample": [{"anomalous_value_timestamp": "1970-01-01T00:00:00", "anomalous_value_row_count": 0.0, "anomalous_value_average_row_count": 5.8, "platform": "ios", "version": "0"}, {"anomalous_value_timestamp": "1970-01-01T00:00:00", "anomalous_value_row_count": 1.0, "anomalous_value_average_row_count": 6.8, "platform": "ios", "version": "1"}, {"anomalous_value_timestamp": "1970-01-01T00:00:00", "anomalous_value_row_count": 0.0, "anomalous_value_average_row_count": 6.8, "platform": "ios", "version": "2"}]}, "result_description": "The last dimension value for dimension platform; version - ios; 2 is 0. The average for this metric is 6.767."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_dimension_anomalies_dimension_anomalies_platform__platform_android_.89f503656a", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "dimension_anomalies", "column_name": null, "test_name": "dimension_anomalies", "test_display_name": "Dimension Anomalies", "latest_run_time": "2023-01-02T12:45:56+02:00", "latest_run_time_utc": "2023-01-02T10:45:56+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.dimension_anomalies", "table_unique_id": "elementary_tests.elon_test.dimension_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n\n \n \n \n \n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n select * from (with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_dimension_anomalies_dimension_anomalies_platform__platform_android___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value)\n where is_anomalous = true", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n\n \n \n \n \n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n select * from (with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_dimension_anomalies_dimension_anomalies_platform__platform_android___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value)\n where is_anomalous = true"}, "configuration": {"test_name": "dimension_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": [], "result_description": null}}], "source.elementary_integration_tests.validation.any_type_column_anomalies_validation": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.source_generic_test_on_column_validation_any_type_column_anomalies_validation_null_count_int.13c361724d", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies_validation", "column_name": "null_count_int", "test_name": "generic_test_on_column", "test_display_name": "Generic Test On Column", "latest_run_time": "2023-01-02T12:46:31+02:00", "latest_run_time_utc": "2023-01-02T10:46:31+00:00", "latest_run_status": "fail", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies_validation", "test_type": "dbt_test", "test_sub_type": "generic", "test_query": "with nothing as (select 1 as num)\n select * from nothing where num = 1", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "Got 1 result, configured to fail if != 0", "result_query": "with nothing as (select 1 as num)\n select * from nothing where num = 1"}, "configuration": {"test_name": "generic_test_on_column", "test_params": null}}, "test_results": {"display_name": "generic_test_on_column", "results_sample": [{"num": 1.0}], "error_message": "Got 1 result, configured to fail if != 0", "failed_rows_count": 1}}], "model.elementary_integration_tests.stats_team": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_schema_changes_stats_team_.fe2147cc27", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "stats_team", "column_name": null, "test_name": "schema_changes", "test_display_name": "Schema Changes", "latest_run_time": "2023-01-02T12:43:14+02:00", "latest_run_time_utc": "2023-01-02T10:43:14+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.stats_team", "table_unique_id": "elementary_tests.elon_test.stats_team", "test_type": "schema_change", "test_sub_type": "generic", "test_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.elementary_test_results\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n \n\n \n \n \n\n \n \n \n\n \n \n \n\n \n\n \n \n \n \n select * from ELEMENTARY_TESTS.elon_test_elementary.elementary_schema_changes_stats_team___schema_changes_alerts", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.elementary_test_results\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n \n\n \n \n \n\n \n \n \n\n \n \n \n\n \n\n \n \n \n \n select * from ELEMENTARY_TESTS.elon_test_elementary.elementary_schema_changes_stats_team___schema_changes_alerts"}, "configuration": {"test_name": "schema_changes", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_schema_changes_stats_team_.fe2147cc27", "database_name": "ELEMENTARY_TESTS", "schema_name": "ELON_TEST", "table_name": "STATS_TEAM", "column_name": "GOALS", "test_name": "schema_changes", "test_display_name": "Schema Changes", "latest_run_time": "2023-01-02T12:46:14+02:00", "latest_run_time_utc": "2023-01-02T10:46:14+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.stats_team", "table_unique_id": "elementary_tests.elon_test.stats_team", "test_type": "schema_change", "test_sub_type": "type_changed", "test_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.elementary_test_results\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n \n\n \n \n \n\n \n \n \n\n \n \n \n\n \n\n \n \n \n \n select * from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_SCHEMA_CHANGES_STATS_TEAM___SCHEMA_CHANGES_ALERTS\"", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "The type of \"GOALS\" was changed from NUMBER to TEXT", "result_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.elementary_test_results\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n \n\n \n \n \n\n \n \n \n\n \n \n \n\n \n\n \n \n \n \n select * from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_SCHEMA_CHANGES_STATS_TEAM___SCHEMA_CHANGES_ALERTS\""}, "configuration": {"test_name": "schema_changes", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_results": {"display_name": "type changed", "metrics": null, "result_description": "The type of \"GOALS\" was changed from NUMBER to TEXT"}}]}, "test_results_totals": {"model.elementary_integration_tests.numeric_column_anomalies": {"errors": 0, "warnings": 0, "passed": 9, "failures": 18}, "model.elementary_integration_tests.any_type_column_anomalies": {"errors": 0, "warnings": 1, "passed": 17, "failures": 40}, "model.elementary_integration_tests.stats_players": {"errors": 0, "warnings": 0, "passed": 1, "failures": 5}, "source.elementary_integration_tests.training.numeric_column_anomalies_training": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "source.elementary_integration_tests.training.any_type_column_anomalies_training": {"errors": 0, "warnings": 0, "passed": 2, "failures": 0}, "model.elementary_integration_tests.string_column_anomalies": {"errors": 0, "warnings": 0, "passed": 10, "failures": 12}, "null": {"errors": 0, "warnings": 0, "passed": 0, "failures": 2}, "source.elementary_integration_tests.training.string_column_anomalies_training": {"errors": 0, "warnings": 0, "passed": 0, "failures": 2}, "model.elementary_integration_tests.groups": {"errors": 0, "warnings": 0, "passed": 1, "failures": 3}, "model.elementary_integration_tests.error_model": {"errors": 1, "warnings": 0, "passed": 0, "failures": 0}, "model.elementary_integration_tests.dimension_anomalies": {"errors": 0, "warnings": 0, "passed": 1, "failures": 2}, "source.elementary_integration_tests.validation.any_type_column_anomalies_validation": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "model.elementary_integration_tests.stats_team": {"errors": 0, "warnings": 0, "passed": 1, "failures": 1}}, "test_runs": {"model.elementary_integration_tests.numeric_column_anomalies": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": "ZERO_PERCENT", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:24+02:00", "latest_run_time_utc": "2023-01-02T10:44:24+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "min", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_zero_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('min' as varchar)\n and upper(column_name) = upper(cast('ZERO_PERCENT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column ZERO_PERCENT, the last min value is 0. The average for this metric is 0.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_zero_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('min' as varchar)\n and upper(column_name) = upper(cast('ZERO_PERCENT' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:44:24+00:00", "id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "model.elementary_integration_tests.numeric_column_anomalies.elementary_freshness_anomalies_numeric_column_anomalies_.freshness_anomalies", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": null, "test_name": "freshness_anomalies", "test_display_name": "Freshness Anomalies", "latest_run_time": "2023-01-02T12:42:19+02:00", "latest_run_time_utc": "2023-01-02T10:42:19+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "freshness", "test_query": "select * from (\n \n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_FRESHNESS_ANOMALIES_NUMERIC_COLUMN_ANOMALIES___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n\n \n\n\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('freshness' as varchar)", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "Last update was at 1969-12-30 00:00:00.000, 48 hours ago. Usually the table is updated within 24.8 hours.", "result_query": "select * from (\n \n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_FRESHNESS_ANOMALIES_NUMERIC_COLUMN_ANOMALIES___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n\n \n\n\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('freshness' as varchar)"}, "configuration": {"test_name": "freshness_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 3}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:41:46+00:00", "id": "1579f298-0625-44b1-9374-a1cfe7a3983c.test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "status": "fail"}, {"affected_rows": null, "time_utc": "2023-01-02T10:42:05+00:00", "id": "532358f7-914c-452d-af6f-ecabf48a4444.test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "status": "fail"}, {"affected_rows": null, "time_utc": "2023-01-02T10:42:19+00:00", "id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "status": "fail"}], "description": "There were 3 failures, no errors and no warnings on the last 3 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": "MAX", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:22+02:00", "latest_run_time_utc": "2023-01-02T10:44:22+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "average", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_average__max__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('MAX' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column MAX, the last average value is 204.19. The average for this metric is 152.37.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_average__max__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('MAX' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:44:22+00:00", "id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": "STANDARD_DEVIATION", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:25+02:00", "latest_run_time_utc": "2023-01-02T10:44:25+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_standard_deviation__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('STANDARD_DEVIATION' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column STANDARD_DEVIATION, the last null_count value is 0. The average for this metric is 0.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_standard_deviation__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('STANDARD_DEVIATION' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:44:25+00:00", "id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": "ZERO_PERCENT", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:24+02:00", "latest_run_time_utc": "2023-01-02T10:44:24+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_zero_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('ZERO_PERCENT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column ZERO_PERCENT, the last null_count value is 0. The average for this metric is 0.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_zero_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('ZERO_PERCENT' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:44:24+00:00", "id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": "STANDARD_DEVIATION", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:25+02:00", "latest_run_time_utc": "2023-01-02T10:44:25+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "standard_deviation", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_standard_deviation__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('standard_deviation' as varchar)\n and upper(column_name) = upper(cast('STANDARD_DEVIATION' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column STANDARD_DEVIATION, the last standard_deviation value is 11.559. The average for this metric is 1.174.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_standard_deviation__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('standard_deviation' as varchar)\n and upper(column_name) = upper(cast('STANDARD_DEVIATION' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:44:25+00:00", "id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": "ZERO_PERCENT", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:24+02:00", "latest_run_time_utc": "2023-01-02T10:44:24+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "variance", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_zero_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('variance' as varchar)\n and upper(column_name) = upper(cast('ZERO_PERCENT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column ZERO_PERCENT, the last variance value is 5856.389. The average for this metric is 4295.229.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_zero_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('variance' as varchar)\n and upper(column_name) = upper(cast('ZERO_PERCENT' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:44:24+00:00", "id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": "STANDARD_DEVIATION", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:25+02:00", "latest_run_time_utc": "2023-01-02T10:44:25+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "variance", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_standard_deviation__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('variance' as varchar)\n and upper(column_name) = upper(cast('STANDARD_DEVIATION' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column STANDARD_DEVIATION, the last variance value is 133.605. The average for this metric is 5.097.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_standard_deviation__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('variance' as varchar)\n and upper(column_name) = upper(cast('STANDARD_DEVIATION' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:44:25+00:00", "id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "model.elementary_integration_tests.numeric_column_anomalies.elementary_volume_anomalies_numeric_column_anomalies_.volume_anomalies", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": null, "test_name": "volume_anomalies", "test_display_name": "Volume Anomalies", "latest_run_time": "2023-01-02T12:42:20+02:00", "latest_run_time_utc": "2023-01-02T10:42:20+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "row_count", "test_query": "select * from (\n \n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_VOLUME_ANOMALIES_NUMERIC_COLUMN_ANOMALIES___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n\n \n\n\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('row_count' as varchar)", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "The last row_count value is 0. The average for this metric is 193.333.", "result_query": "select * from (\n \n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_VOLUME_ANOMALIES_NUMERIC_COLUMN_ANOMALIES___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n\n \n\n\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('row_count' as varchar)"}, "configuration": {"test_name": "volume_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 3}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:41:46+00:00", "id": "1579f298-0625-44b1-9374-a1cfe7a3983c.test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "status": "fail"}, {"affected_rows": null, "time_utc": "2023-01-02T10:42:05+00:00", "id": "532358f7-914c-452d-af6f-ecabf48a4444.test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "status": "fail"}, {"affected_rows": null, "time_utc": "2023-01-02T10:42:20+00:00", "id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "status": "fail"}], "description": "There were 3 failures, no errors and no warnings on the last 3 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": "STANDARD_DEVIATION", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:25+02:00", "latest_run_time_utc": "2023-01-02T10:44:25+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "zero_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_standard_deviation__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('zero_percent' as varchar)\n and upper(column_name) = upper(cast('STANDARD_DEVIATION' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column STANDARD_DEVIATION, the last zero_percent value is 0. The average for this metric is 0.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_standard_deviation__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('zero_percent' as varchar)\n and upper(column_name) = upper(cast('STANDARD_DEVIATION' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:44:25+00:00", "id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": "AVERAGE", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:23+02:00", "latest_run_time_utc": "2023-01-02T10:44:23+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "min", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_min__average__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('min' as varchar)\n and upper(column_name) = upper(cast('AVERAGE' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column AVERAGE, the last min value is 101. The average for this metric is 99.067.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_min__average__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('min' as varchar)\n and upper(column_name) = upper(cast('AVERAGE' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:44:23+00:00", "id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": "ZERO_PERCENT", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:24+02:00", "latest_run_time_utc": "2023-01-02T10:44:24+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "zero_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_zero_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('zero_percent' as varchar)\n and upper(column_name) = upper(cast('ZERO_PERCENT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column ZERO_PERCENT, the last zero_percent value is 61. The average for this metric is 21.25.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_zero_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('zero_percent' as varchar)\n and upper(column_name) = upper(cast('ZERO_PERCENT' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:44:24+00:00", "id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": "STANDARD_DEVIATION", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:25+02:00", "latest_run_time_utc": "2023-01-02T10:44:25+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "average", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_standard_deviation__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('STANDARD_DEVIATION' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column STANDARD_DEVIATION, the last average value is 99.555. The average for this metric is 99.97.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_standard_deviation__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('STANDARD_DEVIATION' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:44:25+00:00", "id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": "ZERO_PERCENT", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:24+02:00", "latest_run_time_utc": "2023-01-02T10:44:24+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "zero_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_zero_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('zero_count' as varchar)\n and upper(column_name) = upper(cast('ZERO_PERCENT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column ZERO_PERCENT, the last zero_count value is 122. The average for this metric is 42.5.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_zero_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('zero_count' as varchar)\n and upper(column_name) = upper(cast('ZERO_PERCENT' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:44:24+00:00", "id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.singular_test_with_one_ref", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": null, "test_name": "singular_test_with_one_ref", "test_display_name": "Singular Test With One Ref", "latest_run_time": "2023-01-02T12:46:31+02:00", "latest_run_time_utc": "2023-01-02T10:46:31+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "dbt_test", "test_sub_type": "singular", "test_query": "select min from ELEMENTARY_TESTS.elon_test.numeric_column_anomalies where min < 100", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "Got 95 results, configured to fail if != 0", "result_query": "select min from ELEMENTARY_TESTS.elon_test.numeric_column_anomalies where min < 100"}, "configuration": {"test_name": "singular_test_with_one_ref", "test_params": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": 95, "time_utc": "2023-01-02T10:46:31+00:00", "id": "e1390d78-5bab-4d75-982c-13520970d8ae.test.elementary_integration_tests.singular_test_with_one_ref", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": "AVERAGE", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:22+02:00", "latest_run_time_utc": "2023-01-02T10:44:22+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "average", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_average__average__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('AVERAGE' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column AVERAGE, the last average value is 105.805. The average for this metric is 100.195.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_average__average__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('AVERAGE' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:44:22+00:00", "id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": "STANDARD_DEVIATION", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:25+02:00", "latest_run_time_utc": "2023-01-02T10:44:25+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "max", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_standard_deviation__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('max' as varchar)\n and upper(column_name) = upper(cast('STANDARD_DEVIATION' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column STANDARD_DEVIATION, the last max value is 120. The average for this metric is 101.633.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_standard_deviation__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('max' as varchar)\n and upper(column_name) = upper(cast('STANDARD_DEVIATION' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:44:25+00:00", "id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": "STANDARD_DEVIATION", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:25+02:00", "latest_run_time_utc": "2023-01-02T10:44:25+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_standard_deviation__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('STANDARD_DEVIATION' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column STANDARD_DEVIATION, the last null_percent value is 0. The average for this metric is 0.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_standard_deviation__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('STANDARD_DEVIATION' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:44:25+00:00", "id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": "STANDARD_DEVIATION", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:25+02:00", "latest_run_time_utc": "2023-01-02T10:44:25+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "min", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_standard_deviation__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('min' as varchar)\n and upper(column_name) = upper(cast('STANDARD_DEVIATION' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column STANDARD_DEVIATION, the last min value is 80. The average for this metric is 98.367.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_standard_deviation__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('min' as varchar)\n and upper(column_name) = upper(cast('STANDARD_DEVIATION' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:44:25+00:00", "id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": "MIN", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:22+02:00", "latest_run_time_utc": "2023-01-02T10:44:22+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "average", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_average__min__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('MIN' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column MIN, the last average value is 102.795. The average for this metric is 148.295.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_average__min__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('MIN' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:44:22+00:00", "id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": "AVERAGE", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:23+02:00", "latest_run_time_utc": "2023-01-02T10:44:23+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "max", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_max__average__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('max' as varchar)\n and upper(column_name) = upper(cast('AVERAGE' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column AVERAGE, the last max value is 110. The average for this metric is 101.3.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_max__average__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('max' as varchar)\n and upper(column_name) = upper(cast('AVERAGE' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:44:23+00:00", "id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": "ZERO_PERCENT", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:24+02:00", "latest_run_time_utc": "2023-01-02T10:44:24+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_zero_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('ZERO_PERCENT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column ZERO_PERCENT, the last null_percent value is 0. The average for this metric is 0.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_zero_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('ZERO_PERCENT' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:44:24+00:00", "id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": "ZERO_PERCENT", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:24+02:00", "latest_run_time_utc": "2023-01-02T10:44:24+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "standard_deviation", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_zero_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('standard_deviation' as varchar)\n and upper(column_name) = upper(cast('ZERO_PERCENT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column ZERO_PERCENT, the last standard_deviation value is 76.527. The average for this metric is 65.456.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_zero_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('standard_deviation' as varchar)\n and upper(column_name) = upper(cast('ZERO_PERCENT' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:44:24+00:00", "id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": "ZERO_PERCENT", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:24+02:00", "latest_run_time_utc": "2023-01-02T10:44:24+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "max", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_zero_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('max' as varchar)\n and upper(column_name) = upper(cast('ZERO_PERCENT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column ZERO_PERCENT, the last max value is 199. The average for this metric is 199.633.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_zero_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('max' as varchar)\n and upper(column_name) = upper(cast('ZERO_PERCENT' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:44:24+00:00", "id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_schema_changes_numeric_column_anomalies_.1d9f82cc93", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": null, "test_name": "schema_changes", "test_display_name": "Schema Changes", "latest_run_time": "2023-01-02T12:46:17+02:00", "latest_run_time_utc": "2023-01-02T10:46:17+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "schema_change", "test_sub_type": "generic", "test_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.elementary_test_results\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n \n\n \n \n \n\n \n \n \n\n \n \n \n\n \n\n \n \n \n \n select * from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_SCHEMA_CHANGES_NUMERIC_COLUMN_ANOMALIES___SCHEMA_CHANGES_ALERTS\"", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.elementary_test_results\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n \n\n \n \n \n\n \n \n \n\n \n \n \n\n \n\n \n \n \n \n select * from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_SCHEMA_CHANGES_NUMERIC_COLUMN_ANOMALIES___SCHEMA_CHANGES_ALERTS\""}, "configuration": {"test_name": "schema_changes", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 2, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:43:14+00:00", "id": "4ea2ffbd-a9b9-46b5-85d1-64b25e74c36a.test.elementary_integration_tests.elementary_schema_changes_numeric_column_anomalies_.1d9f82cc93", "status": "pass"}, {"affected_rows": null, "time_utc": "2023-01-02T10:46:17+00:00", "id": "6a2a0262-5ab7-44ef-af24-fdfefebe0068.test.elementary_integration_tests.elementary_schema_changes_numeric_column_anomalies_.1d9f82cc93", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 2 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": "STANDARD_DEVIATION", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:25+02:00", "latest_run_time_utc": "2023-01-02T10:44:25+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "zero_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_standard_deviation__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('zero_count' as varchar)\n and upper(column_name) = upper(cast('STANDARD_DEVIATION' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column STANDARD_DEVIATION, the last zero_count value is 0. The average for this metric is 0.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_standard_deviation__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('zero_count' as varchar)\n and upper(column_name) = upper(cast('STANDARD_DEVIATION' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:44:25+00:00", "id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies", "column_name": "ZERO_PERCENT", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:24+02:00", "latest_run_time_utc": "2023-01-02T10:44:24+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "average", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_zero_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('ZERO_PERCENT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column ZERO_PERCENT, the last average value is 59.135. The average for this metric is 117.842.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_numeric_column_anomalies_zero_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.NUMERIC_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('ZERO_PERCENT' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:44:24+00:00", "id": "b8e88b26-0ecf-4c6a-b341-00c90265fc94.test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}], "model.elementary_integration_tests.any_type_column_anomalies": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "average", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_INT, the last average value is 151.433. The average for this metric is 149.992.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_FLOAT, the last null_count value is 240. The average for this metric is 58.7.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_INT, the last null_percent value is 80. The average for this metric is 5.567.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "UPDATED_AT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('UPDATED_AT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column UPDATED_AT, the last null_count value is 0. The average for this metric is 0.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('UPDATED_AT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "min_length", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('min_length' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_STR, the last min_length value is 5. The average for this metric is 5.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('min_length' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "model.elementary_integration_tests.any_type_column_anomalies.elementary_volume_anomalies_any_type_column_anomalies_hour__4.volume_anomalies", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": null, "test_name": "volume_anomalies", "test_display_name": "Volume Anomalies", "latest_run_time": "2023-01-02T12:42:20+02:00", "latest_run_time_utc": "2023-01-02T10:42:20+00:00", "latest_run_status": "warn", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "row_count", "test_query": "select * from (\n \n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_VOLUME_ANOMALIES_ANY_TYPE_COLUMN_ANOMALIES_HOUR__4__ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n\n \n\n\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('row_count' as varchar)", "test_params": {}, "test_created_at": null, "description": "This is a very weird description with breaklines and comma, and even a string like this 'wow'. You know, these $##$34#@#!^ can also be helpful WDYT?\n", "result": {"result_description": "The last row_count value is 0. The average for this metric is 281.667.", "result_query": "select * from (\n \n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_VOLUME_ANOMALIES_ANY_TYPE_COLUMN_ANOMALIES_HOUR__4__ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n\n \n\n\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('row_count' as varchar)"}, "configuration": {"test_name": "volume_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 3, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:41:46+00:00", "id": "1579f298-0625-44b1-9374-a1cfe7a3983c.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "status": "warn"}, {"affected_rows": null, "time_utc": "2023-01-02T10:42:05+00:00", "id": "532358f7-914c-452d-af6f-ecabf48a4444.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "status": "warn"}, {"affected_rows": null, "time_utc": "2023-01-02T10:42:20+00:00", "id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "status": "warn"}], "description": "There were no failures, no errors and 3 warnings on the last 3 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_INT, the last null_count value is 240. The average for this metric is 58.7.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "standard_deviation", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('standard_deviation' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_FLOAT, the last standard_deviation value is 2.34. The average for this metric is 2.227.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('standard_deviation' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "max", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('max' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_FLOAT, the last max value is 8.9. The average for this metric is 8.891.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('max' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "missing_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('missing_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_STR, the last missing_count value is 175. The average for this metric is 346.067.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('missing_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "UPDATED_AT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('UPDATED_AT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column UPDATED_AT, the last null_percent value is 0. The average for this metric is 0.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('UPDATED_AT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "max", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('max' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_FLOAT, the last max value is 8.87. The average for this metric is 8.893.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('max' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_STR, the last null_count value is 240. The average for this metric is 58.7.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "average", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_FLOAT, the last average value is 5.022. The average for this metric is 5.043.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "variance", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('variance' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_FLOAT, the last variance value is 5.476. The average for this metric is 4.96.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('variance' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_FLOAT, the last null_percent value is 80. The average for this metric is 5.567.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "min_length", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('min_length' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_STR, the last min_length value is 5. The average for this metric is 5.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('min_length' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "average", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_INT, the last average value is 150.948. The average for this metric is 150.016.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_BOOL", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_BOOL' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_BOOL, the last null_percent value is 80. The average for this metric is 5.567.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_BOOL' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_STR, the last null_percent value is 80. The average for this metric is 5.567.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "variance", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('variance' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_FLOAT, the last variance value is 5.206. The average for this metric is 4.956.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('variance' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "min", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('min' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_FLOAT, the last min value is 1.205. The average for this metric is 1.205.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('min' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "missing_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('missing_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_STR, the last missing_percent value is 58.333. The average for this metric is 21.43.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('missing_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "zero_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('zero_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_FLOAT, the last zero_percent value is 80. The average for this metric is 5.567.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('zero_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_BOOL", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_BOOL' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_BOOL, the last null_count value is 240. The average for this metric is 58.7.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_BOOL' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "zero_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('zero_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_FLOAT, the last zero_count value is 240. The average for this metric is 58.7.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('zero_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_INT, the last null_count value is 204. The average for this metric is 342.5.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "min", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('min' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_INT, the last min value is 103. The average for this metric is 100.1.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('min' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "min", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('min' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_FLOAT, the last min value is 1.201. The average for this metric is 1.207.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('min' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "missing_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('missing_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_STR, the last missing_percent value is 80. The average for this metric is 5.567.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('missing_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_BOOL", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_BOOL' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_BOOL, the last null_count value is 175. The average for this metric is 339.533.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_BOOL' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "average_length", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average_length' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_STR, the last average_length value is 5. The average for this metric is 5.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average_length' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "standard_deviation", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('standard_deviation' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_INT, the last standard_deviation value is 30.033. The average for this metric is 29.182.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('standard_deviation' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "max_length", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('max_length' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_STR, the last max_length value is 5. The average for this metric is 5.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('max_length' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_INT, the last null_percent value is 68. The average for this metric is 21.426.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "standard_deviation", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('standard_deviation' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_FLOAT, the last standard_deviation value is 2.282. The average for this metric is 2.226.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('standard_deviation' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "average", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_FLOAT, the last average value is 5.094. The average for this metric is 5.062.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "zero_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('zero_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_INT, the last zero_count value is 204. The average for this metric is 342.5.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('zero_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "zero_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('zero_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_INT, the last zero_count value is 240. The average for this metric is 58.7.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('zero_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "max", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('max' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_INT, the last max value is 200. The average for this metric is 200.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('max' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "variance", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('variance' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_INT, the last variance value is 901.987. The average for this metric is 851.775.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('variance' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "zero_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('zero_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_FLOAT, the last zero_percent value is 62.333. The average for this metric is 21.854.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('zero_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "variance", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('variance' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_INT, the last variance value is 972.623. The average for this metric is 856.161.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('variance' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "average_length", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average_length' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_STR, the last average_length value is 5. The average for this metric is 5.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average_length' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "missing_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('missing_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_STR, the last missing_count value is 240. The average for this metric is 58.7.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('missing_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "zero_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('zero_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_INT, the last zero_percent value is 68. The average for this metric is 21.426.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('zero_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_FLOAT, the last null_count value is 187. The average for this metric is 349.533.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_FLOAT, the last null_percent value is 62.333. The average for this metric is 21.854.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "model.elementary_integration_tests.any_type_column_anomalies.generic_test_on_model_any_type_column_anomalies_.generic_test_on_model", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": null, "test_name": "generic_test_on_model", "test_display_name": "Generic Test On Model", "latest_run_time": "2023-01-02T12:46:31+02:00", "latest_run_time_utc": "2023-01-02T10:46:31+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "dbt_test", "test_sub_type": "generic", "test_query": "with nothing as (select 1 as num)\n select * from nothing where num = 1", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "Got 1 result, configured to fail if != 0", "result_query": "with nothing as (select 1 as num)\n select * from nothing where num = 1"}, "configuration": {"test_name": "generic_test_on_model", "test_params": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": 1, "time_utc": "2023-01-02T10:46:31+00:00", "id": "e1390d78-5bab-4d75-982c-13520970d8ae.test.elementary_integration_tests.generic_test_on_model_any_type_column_anomalies_.a9e77d8087", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "standard_deviation", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('standard_deviation' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_INT, the last standard_deviation value is 31.187. The average for this metric is 29.256.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('standard_deviation' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "max", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('max' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_INT, the last max value is 198. The average for this metric is 199.933.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('max' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_BOOL", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_BOOL' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_BOOL, the last null_percent value is 58.333. The average for this metric is 21.131.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_BOOL' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "zero_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('zero_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_INT, the last zero_percent value is 80. The average for this metric is 5.567.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('zero_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "zero_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('zero_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_FLOAT, the last zero_count value is 187. The average for this metric is 349.533.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('zero_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_STR, the last null_percent value is 58.333. The average for this metric is 21.43.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "min", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('min' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_INT, the last min value is 101. The average for this metric is 100.033.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('min' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_PERCENT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_PERCENT_STR, the last null_count value is 175. The average for this metric is 346.067.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies", "column_name": "NULL_COUNT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-01-02T12:45:29+02:00", "latest_run_time_utc": "2023-01-02T10:45:29+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "max_length", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('max_length' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column NULL_COUNT_STR, the last max_length value is 5. The average for this metric is 5.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.ANY_TYPE_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('max_length' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:29+00:00", "id": "46220afb-ae61-4e3d-b6a9-f42ceae582a2.test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}], "model.elementary_integration_tests.stats_players": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_schema_changes_stats_players_.388c33d77b", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "stats_players", "column_name": null, "test_name": "schema_changes", "test_display_name": "Schema Changes", "latest_run_time": "2023-01-02T12:43:14+02:00", "latest_run_time_utc": "2023-01-02T10:43:14+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.stats_players", "table_unique_id": "elementary_tests.elon_test.stats_players", "test_type": "schema_change", "test_sub_type": "generic", "test_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.elementary_test_results\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n \n\n \n \n \n\n \n \n \n\n \n \n \n\n \n\n \n \n \n \n select * from ELEMENTARY_TESTS.elon_test_elementary.elementary_schema_changes_stats_players___schema_changes_alerts", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.elementary_test_results\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n \n\n \n \n \n\n \n \n \n\n \n \n \n\n \n\n \n \n \n \n select * from ELEMENTARY_TESTS.elon_test_elementary.elementary_schema_changes_stats_players___schema_changes_alerts"}, "configuration": {"test_name": "schema_changes", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:43:14+00:00", "id": "4ea2ffbd-a9b9-46b5-85d1-64b25e74c36a.test.elementary_integration_tests.elementary_schema_changes_stats_players_.388c33d77b", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_schema_changes_stats_players_.388c33d77b", "database_name": "ELEMENTARY_TESTS", "schema_name": "ELON_TEST", "table_name": "STATS_PLAYERS", "column_name": "KEY_CROSSES", "test_name": "schema_changes", "test_display_name": "Schema Changes", "latest_run_time": "2023-01-02T12:46:13+02:00", "latest_run_time_utc": "2023-01-02T10:46:13+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.stats_players", "table_unique_id": "elementary_tests.elon_test.stats_players", "test_type": "schema_change", "test_sub_type": "column_added", "test_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.elementary_test_results\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n \n\n \n \n \n\n \n \n \n\n \n \n \n\n \n\n \n \n \n \n select * from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_SCHEMA_CHANGES_STATS_PLAYERS___SCHEMA_CHANGES_ALERTS\"", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "The column \"KEY_CROSSES\" was added", "result_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.elementary_test_results\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n \n\n \n \n \n\n \n \n \n\n \n \n \n\n \n\n \n \n \n \n select * from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_SCHEMA_CHANGES_STATS_PLAYERS___SCHEMA_CHANGES_ALERTS\""}, "configuration": {"test_name": "schema_changes", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:46:13+00:00", "id": "6a2a0262-5ab7-44ef-af24-fdfefebe0068.test.elementary_integration_tests.elementary_schema_changes_stats_players_.388c33d77b", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_schema_changes_from_baseline_stats_players_.02157887f9", "database_name": "ELEMENTARY_TESTS", "schema_name": "ELON_TEST", "table_name": "STATS_PLAYERS", "column_name": "coffee_cups_consumed", "test_name": "schema_changes_from_baseline", "test_display_name": "Schema Changes From Baseline", "latest_run_time": "2023-01-02T12:43:23+02:00", "latest_run_time_utc": "2023-01-02T10:43:23+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.stats_players", "table_unique_id": "elementary_tests.elon_test.stats_players", "test_type": "schema_change", "test_sub_type": "column_removed", "test_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n\n \n \n \n\n \n \n \n \n\n \n\n \n\n with cur as (\n \n with baseline as (\n select lower(column_name) as column_name, data_type\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_schema_changes_from_baseline_stats_players___schema_baseline__dbt_tmp\n )\n\n select\n info_schema.full_table_name,\n lower(info_schema.column_name) as column_name,\n info_schema.data_type,\n (baseline.column_name IS NULL) as is_new,\n \n cast ('2023-01-02 10:43:23' as TIMESTAMP)\n as detected_at\n from ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns info_schema\n left join baseline on (\n lower(info_schema.column_name) = lower(baseline.column_name)\n )\n where lower(info_schema.full_table_name) = lower('ELEMENTARY_TESTS.ELON_TEST.STATS_PLAYERS')\n \n ),\n\n pre as (\n \n select\n cast('ELEMENTARY_TESTS.ELON_TEST.STATS_PLAYERS' as varchar) as full_table_name,\n column_name,\n data_type,\n \n cast ('2023-01-02 10:43:23' as TIMESTAMP)\n as detected_at\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_schema_changes_from_baseline_stats_players___schema_baseline__dbt_tmp\n \n ),\n\n type_changes as (\n\n \n select\n cur.full_table_name,\n 'type_changed' as change,\n cur.column_name,\n cur.data_type as data_type,\n pre.data_type as pre_data_type,\n pre.detected_at\n from cur inner join pre\n on (cur.full_table_name = pre.full_table_name and cur.column_name = pre.column_name)\n where pre.data_type IS NOT NULL AND cur.data_type != pre.data_type\n\n ),\n\n \n\n columns_removed as (\n\n \n select\n pre.full_table_name,\n 'column_removed' as change,\n pre.column_name as column_name,\n \n cast(null as varchar)\n as data_type,\n pre.data_type as pre_data_type,\n pre.detected_at as detected_at\n from pre left join cur\n on (cur.full_table_name = pre.full_table_name and cur.column_name = pre.column_name)\n where cur.full_table_name is null and cur.column_name is null\n\n ),\n\n columns_removed_filter_deleted_tables as (\n\n \n select\n removed.full_table_name,\n removed.change,\n removed.column_name,\n removed.data_type,\n removed.pre_data_type,\n removed.detected_at\n from columns_removed as removed join cur\n on (removed.full_table_name = cur.full_table_name)\n\n ),\n\n all_column_changes as (\n\n \n select * from type_changes\n union all\n select * from columns_removed_filter_deleted_tables\n \n ),\n\n column_changes_test_results as (\n\n \n select\n \n \n\n md5(cast(coalesce(cast(full_table_name as varchar), '') || '-' || coalesce(cast(column_name as varchar), '') || '-' || coalesce(cast(change as varchar), '') || '-' || coalesce(cast(detected_at as varchar), '') as TEXT))\n as data_issue_id,\n \n cast ('2023-01-02 10:43:23' as TIMESTAMP)\n as detected_at,\n \n trim(split(full_table_name,'.')[0],'\"') as database_name\n\n,\n \n trim(split(full_table_name,'.')[1],'\"') as schema_name\n\n,\n \n trim(split(full_table_name,'.')[2],'\"') as table_name\n\n,\n column_name,\n 'schema_change' as test_type,\n change as test_sub_type,\n case\n when change = 'column_added'\n then 'The column \"' || column_name || '\" was added'\n when change= 'column_removed'\n then 'The column \"' || column_name || '\" was removed'\n when change= 'type_changed'\n then 'The type of \"' || column_name || '\" was changed from ' || pre_data_type || ' to ' || data_type\n else NULL\n end as test_results_description\n from all_column_changes\n group by 1,2,3,4,5,6,7,8,9\n\n )\n\n \n select \n \n\n md5(cast(coalesce(cast(data_issue_id as varchar), '') || '-' || coalesce(cast(cast('acd0caa6-5efb-4df9-b512-b449e218426f.test.elementary_integration_tests.elementary_schema_changes_from_baseline_stats_players_.02157887f9' as varchar) as varchar), '') as TEXT))\n as id,\n cast('acd0caa6-5efb-4df9-b512-b449e218426f.test.elementary_integration_tests.elementary_schema_changes_from_baseline_stats_players_.02157887f9' as varchar) as test_execution_id,\n cast('test.elementary_integration_tests.elementary_schema_changes_from_baseline_stats_players_.02157887f9' as varchar) as test_unique_id,\n *\n from column_changes_test_results", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "The column \"coffee_cups_consumed\" was removed", "result_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n\n \n \n \n\n \n \n \n \n\n \n\n \n\n with cur as (\n \n with baseline as (\n select lower(column_name) as column_name, data_type\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_schema_changes_from_baseline_stats_players___schema_baseline__dbt_tmp\n )\n\n select\n info_schema.full_table_name,\n lower(info_schema.column_name) as column_name,\n info_schema.data_type,\n (baseline.column_name IS NULL) as is_new,\n \n cast ('2023-01-02 10:43:23' as TIMESTAMP)\n as detected_at\n from ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns info_schema\n left join baseline on (\n lower(info_schema.column_name) = lower(baseline.column_name)\n )\n where lower(info_schema.full_table_name) = lower('ELEMENTARY_TESTS.ELON_TEST.STATS_PLAYERS')\n \n ),\n\n pre as (\n \n select\n cast('ELEMENTARY_TESTS.ELON_TEST.STATS_PLAYERS' as varchar) as full_table_name,\n column_name,\n data_type,\n \n cast ('2023-01-02 10:43:23' as TIMESTAMP)\n as detected_at\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_schema_changes_from_baseline_stats_players___schema_baseline__dbt_tmp\n \n ),\n\n type_changes as (\n\n \n select\n cur.full_table_name,\n 'type_changed' as change,\n cur.column_name,\n cur.data_type as data_type,\n pre.data_type as pre_data_type,\n pre.detected_at\n from cur inner join pre\n on (cur.full_table_name = pre.full_table_name and cur.column_name = pre.column_name)\n where pre.data_type IS NOT NULL AND cur.data_type != pre.data_type\n\n ),\n\n \n\n columns_removed as (\n\n \n select\n pre.full_table_name,\n 'column_removed' as change,\n pre.column_name as column_name,\n \n cast(null as varchar)\n as data_type,\n pre.data_type as pre_data_type,\n pre.detected_at as detected_at\n from pre left join cur\n on (cur.full_table_name = pre.full_table_name and cur.column_name = pre.column_name)\n where cur.full_table_name is null and cur.column_name is null\n\n ),\n\n columns_removed_filter_deleted_tables as (\n\n \n select\n removed.full_table_name,\n removed.change,\n removed.column_name,\n removed.data_type,\n removed.pre_data_type,\n removed.detected_at\n from columns_removed as removed join cur\n on (removed.full_table_name = cur.full_table_name)\n\n ),\n\n all_column_changes as (\n\n \n select * from type_changes\n union all\n select * from columns_removed_filter_deleted_tables\n \n ),\n\n column_changes_test_results as (\n\n \n select\n \n \n\n md5(cast(coalesce(cast(full_table_name as varchar), '') || '-' || coalesce(cast(column_name as varchar), '') || '-' || coalesce(cast(change as varchar), '') || '-' || coalesce(cast(detected_at as varchar), '') as TEXT))\n as data_issue_id,\n \n cast ('2023-01-02 10:43:23' as TIMESTAMP)\n as detected_at,\n \n trim(split(full_table_name,'.')[0],'\"') as database_name\n\n,\n \n trim(split(full_table_name,'.')[1],'\"') as schema_name\n\n,\n \n trim(split(full_table_name,'.')[2],'\"') as table_name\n\n,\n column_name,\n 'schema_change' as test_type,\n change as test_sub_type,\n case\n when change = 'column_added'\n then 'The column \"' || column_name || '\" was added'\n when change= 'column_removed'\n then 'The column \"' || column_name || '\" was removed'\n when change= 'type_changed'\n then 'The type of \"' || column_name || '\" was changed from ' || pre_data_type || ' to ' || data_type\n else NULL\n end as test_results_description\n from all_column_changes\n group by 1,2,3,4,5,6,7,8,9\n\n )\n\n \n select \n \n\n md5(cast(coalesce(cast(data_issue_id as varchar), '') || '-' || coalesce(cast(cast('acd0caa6-5efb-4df9-b512-b449e218426f.test.elementary_integration_tests.elementary_schema_changes_from_baseline_stats_players_.02157887f9' as varchar) as varchar), '') as TEXT))\n as id,\n cast('acd0caa6-5efb-4df9-b512-b449e218426f.test.elementary_integration_tests.elementary_schema_changes_from_baseline_stats_players_.02157887f9' as varchar) as test_execution_id,\n cast('test.elementary_integration_tests.elementary_schema_changes_from_baseline_stats_players_.02157887f9' as varchar) as test_unique_id,\n *\n from column_changes_test_results"}, "configuration": {"test_name": "schema_changes_from_baseline", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:43:23+00:00", "id": "acd0caa6-5efb-4df9-b512-b449e218426f.test.elementary_integration_tests.elementary_schema_changes_from_baseline_stats_players_.02157887f9", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_schema_changes_from_baseline_stats_players_.02157887f9", "database_name": "ELEMENTARY_TESTS", "schema_name": "ELON_TEST", "table_name": "STATS_PLAYERS", "column_name": "goals", "test_name": "schema_changes_from_baseline", "test_display_name": "Schema Changes From Baseline", "latest_run_time": "2023-01-02T12:43:23+02:00", "latest_run_time_utc": "2023-01-02T10:43:23+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.stats_players", "table_unique_id": "elementary_tests.elon_test.stats_players", "test_type": "schema_change", "test_sub_type": "type_changed", "test_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n\n \n \n \n\n \n \n \n \n\n \n\n \n\n with cur as (\n \n with baseline as (\n select lower(column_name) as column_name, data_type\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_schema_changes_from_baseline_stats_players___schema_baseline__dbt_tmp\n )\n\n select\n info_schema.full_table_name,\n lower(info_schema.column_name) as column_name,\n info_schema.data_type,\n (baseline.column_name IS NULL) as is_new,\n \n cast ('2023-01-02 10:43:23' as TIMESTAMP)\n as detected_at\n from ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns info_schema\n left join baseline on (\n lower(info_schema.column_name) = lower(baseline.column_name)\n )\n where lower(info_schema.full_table_name) = lower('ELEMENTARY_TESTS.ELON_TEST.STATS_PLAYERS')\n \n ),\n\n pre as (\n \n select\n cast('ELEMENTARY_TESTS.ELON_TEST.STATS_PLAYERS' as varchar) as full_table_name,\n column_name,\n data_type,\n \n cast ('2023-01-02 10:43:23' as TIMESTAMP)\n as detected_at\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_schema_changes_from_baseline_stats_players___schema_baseline__dbt_tmp\n \n ),\n\n type_changes as (\n\n \n select\n cur.full_table_name,\n 'type_changed' as change,\n cur.column_name,\n cur.data_type as data_type,\n pre.data_type as pre_data_type,\n pre.detected_at\n from cur inner join pre\n on (cur.full_table_name = pre.full_table_name and cur.column_name = pre.column_name)\n where pre.data_type IS NOT NULL AND cur.data_type != pre.data_type\n\n ),\n\n \n\n columns_removed as (\n\n \n select\n pre.full_table_name,\n 'column_removed' as change,\n pre.column_name as column_name,\n \n cast(null as varchar)\n as data_type,\n pre.data_type as pre_data_type,\n pre.detected_at as detected_at\n from pre left join cur\n on (cur.full_table_name = pre.full_table_name and cur.column_name = pre.column_name)\n where cur.full_table_name is null and cur.column_name is null\n\n ),\n\n columns_removed_filter_deleted_tables as (\n\n \n select\n removed.full_table_name,\n removed.change,\n removed.column_name,\n removed.data_type,\n removed.pre_data_type,\n removed.detected_at\n from columns_removed as removed join cur\n on (removed.full_table_name = cur.full_table_name)\n\n ),\n\n all_column_changes as (\n\n \n select * from type_changes\n union all\n select * from columns_removed_filter_deleted_tables\n \n ),\n\n column_changes_test_results as (\n\n \n select\n \n \n\n md5(cast(coalesce(cast(full_table_name as varchar), '') || '-' || coalesce(cast(column_name as varchar), '') || '-' || coalesce(cast(change as varchar), '') || '-' || coalesce(cast(detected_at as varchar), '') as TEXT))\n as data_issue_id,\n \n cast ('2023-01-02 10:43:23' as TIMESTAMP)\n as detected_at,\n \n trim(split(full_table_name,'.')[0],'\"') as database_name\n\n,\n \n trim(split(full_table_name,'.')[1],'\"') as schema_name\n\n,\n \n trim(split(full_table_name,'.')[2],'\"') as table_name\n\n,\n column_name,\n 'schema_change' as test_type,\n change as test_sub_type,\n case\n when change = 'column_added'\n then 'The column \"' || column_name || '\" was added'\n when change= 'column_removed'\n then 'The column \"' || column_name || '\" was removed'\n when change= 'type_changed'\n then 'The type of \"' || column_name || '\" was changed from ' || pre_data_type || ' to ' || data_type\n else NULL\n end as test_results_description\n from all_column_changes\n group by 1,2,3,4,5,6,7,8,9\n\n )\n\n \n select \n \n\n md5(cast(coalesce(cast(data_issue_id as varchar), '') || '-' || coalesce(cast(cast('acd0caa6-5efb-4df9-b512-b449e218426f.test.elementary_integration_tests.elementary_schema_changes_from_baseline_stats_players_.02157887f9' as varchar) as varchar), '') as TEXT))\n as id,\n cast('acd0caa6-5efb-4df9-b512-b449e218426f.test.elementary_integration_tests.elementary_schema_changes_from_baseline_stats_players_.02157887f9' as varchar) as test_execution_id,\n cast('test.elementary_integration_tests.elementary_schema_changes_from_baseline_stats_players_.02157887f9' as varchar) as test_unique_id,\n *\n from column_changes_test_results", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "The type of \"goals\" was changed from BOOLEAN to NUMBER", "result_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n\n \n \n \n\n \n \n \n \n\n \n\n \n\n with cur as (\n \n with baseline as (\n select lower(column_name) as column_name, data_type\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_schema_changes_from_baseline_stats_players___schema_baseline__dbt_tmp\n )\n\n select\n info_schema.full_table_name,\n lower(info_schema.column_name) as column_name,\n info_schema.data_type,\n (baseline.column_name IS NULL) as is_new,\n \n cast ('2023-01-02 10:43:23' as TIMESTAMP)\n as detected_at\n from ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns info_schema\n left join baseline on (\n lower(info_schema.column_name) = lower(baseline.column_name)\n )\n where lower(info_schema.full_table_name) = lower('ELEMENTARY_TESTS.ELON_TEST.STATS_PLAYERS')\n \n ),\n\n pre as (\n \n select\n cast('ELEMENTARY_TESTS.ELON_TEST.STATS_PLAYERS' as varchar) as full_table_name,\n column_name,\n data_type,\n \n cast ('2023-01-02 10:43:23' as TIMESTAMP)\n as detected_at\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_schema_changes_from_baseline_stats_players___schema_baseline__dbt_tmp\n \n ),\n\n type_changes as (\n\n \n select\n cur.full_table_name,\n 'type_changed' as change,\n cur.column_name,\n cur.data_type as data_type,\n pre.data_type as pre_data_type,\n pre.detected_at\n from cur inner join pre\n on (cur.full_table_name = pre.full_table_name and cur.column_name = pre.column_name)\n where pre.data_type IS NOT NULL AND cur.data_type != pre.data_type\n\n ),\n\n \n\n columns_removed as (\n\n \n select\n pre.full_table_name,\n 'column_removed' as change,\n pre.column_name as column_name,\n \n cast(null as varchar)\n as data_type,\n pre.data_type as pre_data_type,\n pre.detected_at as detected_at\n from pre left join cur\n on (cur.full_table_name = pre.full_table_name and cur.column_name = pre.column_name)\n where cur.full_table_name is null and cur.column_name is null\n\n ),\n\n columns_removed_filter_deleted_tables as (\n\n \n select\n removed.full_table_name,\n removed.change,\n removed.column_name,\n removed.data_type,\n removed.pre_data_type,\n removed.detected_at\n from columns_removed as removed join cur\n on (removed.full_table_name = cur.full_table_name)\n\n ),\n\n all_column_changes as (\n\n \n select * from type_changes\n union all\n select * from columns_removed_filter_deleted_tables\n \n ),\n\n column_changes_test_results as (\n\n \n select\n \n \n\n md5(cast(coalesce(cast(full_table_name as varchar), '') || '-' || coalesce(cast(column_name as varchar), '') || '-' || coalesce(cast(change as varchar), '') || '-' || coalesce(cast(detected_at as varchar), '') as TEXT))\n as data_issue_id,\n \n cast ('2023-01-02 10:43:23' as TIMESTAMP)\n as detected_at,\n \n trim(split(full_table_name,'.')[0],'\"') as database_name\n\n,\n \n trim(split(full_table_name,'.')[1],'\"') as schema_name\n\n,\n \n trim(split(full_table_name,'.')[2],'\"') as table_name\n\n,\n column_name,\n 'schema_change' as test_type,\n change as test_sub_type,\n case\n when change = 'column_added'\n then 'The column \"' || column_name || '\" was added'\n when change= 'column_removed'\n then 'The column \"' || column_name || '\" was removed'\n when change= 'type_changed'\n then 'The type of \"' || column_name || '\" was changed from ' || pre_data_type || ' to ' || data_type\n else NULL\n end as test_results_description\n from all_column_changes\n group by 1,2,3,4,5,6,7,8,9\n\n )\n\n \n select \n \n\n md5(cast(coalesce(cast(data_issue_id as varchar), '') || '-' || coalesce(cast(cast('acd0caa6-5efb-4df9-b512-b449e218426f.test.elementary_integration_tests.elementary_schema_changes_from_baseline_stats_players_.02157887f9' as varchar) as varchar), '') as TEXT))\n as id,\n cast('acd0caa6-5efb-4df9-b512-b449e218426f.test.elementary_integration_tests.elementary_schema_changes_from_baseline_stats_players_.02157887f9' as varchar) as test_execution_id,\n cast('test.elementary_integration_tests.elementary_schema_changes_from_baseline_stats_players_.02157887f9' as varchar) as test_unique_id,\n *\n from column_changes_test_results"}, "configuration": {"test_name": "schema_changes_from_baseline", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:43:23+00:00", "id": "acd0caa6-5efb-4df9-b512-b449e218426f.test.elementary_integration_tests.elementary_schema_changes_from_baseline_stats_players_.02157887f9", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_schema_changes_stats_players_.388c33d77b", "database_name": "ELEMENTARY_TESTS", "schema_name": "ELON_TEST", "table_name": "STATS_PLAYERS", "column_name": "OFFSIDES", "test_name": "schema_changes", "test_display_name": "Schema Changes", "latest_run_time": "2023-01-02T12:46:13+02:00", "latest_run_time_utc": "2023-01-02T10:46:13+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.stats_players", "table_unique_id": "elementary_tests.elon_test.stats_players", "test_type": "schema_change", "test_sub_type": "column_removed", "test_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.elementary_test_results\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n \n\n \n \n \n\n \n \n \n\n \n \n \n\n \n\n \n \n \n \n select * from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_SCHEMA_CHANGES_STATS_PLAYERS___SCHEMA_CHANGES_ALERTS\"", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "The column \"OFFSIDES\" was removed", "result_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.elementary_test_results\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n \n\n \n \n \n\n \n \n \n\n \n \n \n\n \n\n \n \n \n \n select * from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_SCHEMA_CHANGES_STATS_PLAYERS___SCHEMA_CHANGES_ALERTS\""}, "configuration": {"test_name": "schema_changes", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:46:13+00:00", "id": "6a2a0262-5ab7-44ef-af24-fdfefebe0068.test.elementary_integration_tests.elementary_schema_changes_stats_players_.388c33d77b", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_schema_changes_stats_players_.388c33d77b", "database_name": "ELEMENTARY_TESTS", "schema_name": "ELON_TEST", "table_name": "STATS_PLAYERS", "column_name": "RED_CARDS", "test_name": "schema_changes", "test_display_name": "Schema Changes", "latest_run_time": "2023-01-02T12:46:13+02:00", "latest_run_time_utc": "2023-01-02T10:46:13+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.stats_players", "table_unique_id": "elementary_tests.elon_test.stats_players", "test_type": "schema_change", "test_sub_type": "column_added", "test_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.elementary_test_results\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n \n\n \n \n \n\n \n \n \n\n \n \n \n\n \n\n \n \n \n \n select * from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_SCHEMA_CHANGES_STATS_PLAYERS___SCHEMA_CHANGES_ALERTS\"", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "The column \"RED_CARDS\" was added", "result_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.elementary_test_results\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n \n\n \n \n \n\n \n \n \n\n \n \n \n\n \n\n \n \n \n \n select * from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_SCHEMA_CHANGES_STATS_PLAYERS___SCHEMA_CHANGES_ALERTS\""}, "configuration": {"test_name": "schema_changes", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:46:13+00:00", "id": "6a2a0262-5ab7-44ef-af24-fdfefebe0068.test.elementary_integration_tests.elementary_schema_changes_stats_players_.388c33d77b", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}], "source.elementary_integration_tests.training.numeric_column_anomalies_training": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.singular_test_with_source_ref", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "numeric_column_anomalies_training", "column_name": null, "test_name": "singular_test_with_source_ref", "test_display_name": "Singular Test With Source Ref", "latest_run_time": "2023-01-02T12:46:31+02:00", "latest_run_time_utc": "2023-01-02T10:46:31+00:00", "latest_run_status": "fail", "model_unique_id": "source.elementary_integration_tests.training.numeric_column_anomalies_training", "table_unique_id": "elementary_tests.elon_test.numeric_column_anomalies_training", "test_type": "dbt_test", "test_sub_type": "singular", "test_query": "select min from ELEMENTARY_TESTS.test_seeds.numeric_column_anomalies_training where min < 105", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "Got 317 results, configured to fail if != 0", "result_query": "select min from ELEMENTARY_TESTS.test_seeds.numeric_column_anomalies_training where min < 105"}, "configuration": {"test_name": "singular_test_with_source_ref", "test_params": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": 317, "time_utc": "2023-01-02T10:46:31+00:00", "id": "e1390d78-5bab-4d75-982c-13520970d8ae.test.elementary_integration_tests.singular_test_with_source_ref", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}], "source.elementary_integration_tests.training.any_type_column_anomalies_training": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_any_type_column_anomalies_training_.e6ec3c50e5", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies_training", "column_name": null, "test_name": "volume_anomalies", "test_display_name": "Volume Anomalies", "latest_run_time": "2023-01-02T12:42:19+02:00", "latest_run_time_utc": "2023-01-02T10:42:19+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.training.any_type_column_anomalies_training", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies_training", "test_type": "anomaly_detection", "test_sub_type": "row_count", "test_query": "select * from (\n \n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_SOURCE_VOLUME_ANOMALIES_TRAINING_ANY_TYPE_COLUMN_ANOMALIES_TRAINING___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n\n \n\n\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_TRAINING' as varchar)) and\n metric_name = cast('row_count' as varchar)", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n \n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_SOURCE_VOLUME_ANOMALIES_TRAINING_ANY_TYPE_COLUMN_ANOMALIES_TRAINING___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n\n \n\n\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_TRAINING' as varchar)) and\n metric_name = cast('row_count' as varchar)"}, "configuration": {"test_name": "volume_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 3, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:41:45+00:00", "id": "1579f298-0625-44b1-9374-a1cfe7a3983c.test.elementary_integration_tests.elementary_source_volume_anomalies_training_any_type_column_anomalies_training_.e6ec3c50e5", "status": "pass"}, {"affected_rows": null, "time_utc": "2023-01-02T10:42:04+00:00", "id": "532358f7-914c-452d-af6f-ecabf48a4444.test.elementary_integration_tests.elementary_source_volume_anomalies_training_any_type_column_anomalies_training_.e6ec3c50e5", "status": "pass"}, {"affected_rows": null, "time_utc": "2023-01-02T10:42:19+00:00", "id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_volume_anomalies_training_any_type_column_anomalies_training_.e6ec3c50e5", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 3 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_any_type_column_anomalies_training_.5733415eda", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies_training", "column_name": null, "test_name": "freshness_anomalies", "test_display_name": "Freshness Anomalies", "latest_run_time": "2023-01-02T12:42:23+02:00", "latest_run_time_utc": "2023-01-02T10:42:23+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.training.any_type_column_anomalies_training", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies_training", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_SOURCE_FRESHNESS_ANOMALIES_TRAINING_ANY_TYPE_COLUMN_ANOMALIES_TRAINING___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_SOURCE_FRESHNESS_ANOMALIES_TRAINING_ANY_TYPE_COLUMN_ANOMALIES_TRAINING___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value"}, "configuration": {"test_name": "freshness_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 3, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:41:49+00:00", "id": "1579f298-0625-44b1-9374-a1cfe7a3983c.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_any_type_column_anomalies_training_.5733415eda", "status": "pass"}, {"affected_rows": null, "time_utc": "2023-01-02T10:42:08+00:00", "id": "532358f7-914c-452d-af6f-ecabf48a4444.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_any_type_column_anomalies_training_.5733415eda", "status": "pass"}, {"affected_rows": null, "time_utc": "2023-01-02T10:42:23+00:00", "id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_any_type_column_anomalies_training_.5733415eda", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 3 test runs."}}], "model.elementary_integration_tests.string_column_anomalies": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "string_column_anomalies", "column_name": "MISSING_PERCENT", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:00+02:00", "latest_run_time_utc": "2023-01-02T10:44:00+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.elon_test.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "average_length", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average_length' as varchar)\n and upper(column_name) = upper(cast('MISSING_PERCENT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column MISSING_PERCENT, the last average_length value is 5. The average for this metric is 5.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average_length' as varchar)\n and upper(column_name) = upper(cast('MISSING_PERCENT' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:44:00+00:00", "id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "string_column_anomalies", "column_name": "MISSING_COUNT", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:00+02:00", "latest_run_time_utc": "2023-01-02T10:44:00+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.elon_test.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "missing_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_count__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('missing_percent' as varchar)\n and upper(column_name) = upper(cast('MISSING_COUNT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column MISSING_COUNT, the last missing_percent value is 20. The average for this metric is 3.567.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_count__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('missing_percent' as varchar)\n and upper(column_name) = upper(cast('MISSING_COUNT' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:44:00+00:00", "id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "string_column_anomalies", "column_name": "MISSING_PERCENT", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:00+02:00", "latest_run_time_utc": "2023-01-02T10:44:00+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.elon_test.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "min_length", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('min_length' as varchar)\n and upper(column_name) = upper(cast('MISSING_PERCENT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column MISSING_PERCENT, the last min_length value is 5. The average for this metric is 5.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('min_length' as varchar)\n and upper(column_name) = upper(cast('MISSING_PERCENT' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:44:00+00:00", "id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.relationships_string_column_anomalies_min_length__max_length__source_training_string_column_anomalies_training_.a2cbc353c6", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "string_column_anomalies", "column_name": "min_length", "test_name": "relationships", "test_display_name": "Relationships", "latest_run_time": "2023-01-02T12:46:31+02:00", "latest_run_time_utc": "2023-01-02T10:46:31+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.elon_test.string_column_anomalies", "test_type": "dbt_test", "test_sub_type": "generic", "test_query": "with child as (\n select min_length as from_field\n from ELEMENTARY_TESTS.elon_test.string_column_anomalies\n where min_length is not null\n),\n\nparent as (\n select max_length as to_field\n from ELEMENTARY_TESTS.test_seeds.string_column_anomalies_training\n)\n\nselect\n from_field\n\nfrom child\nleft join parent\n on child.from_field = parent.to_field\n\nwhere parent.to_field is null", "test_params": {}, "test_created_at": null, "description": "This test validates that all of the records in a child table have a corresponding record in a parent table. This property is referred to as \"referential integrity\".", "result": {"result_description": "Got 3100 results, configured to fail if != 0", "result_query": "with child as (\n select min_length as from_field\n from ELEMENTARY_TESTS.elon_test.string_column_anomalies\n where min_length is not null\n),\n\nparent as (\n select max_length as to_field\n from ELEMENTARY_TESTS.test_seeds.string_column_anomalies_training\n)\n\nselect\n from_field\n\nfrom child\nleft join parent\n on child.from_field = parent.to_field\n\nwhere parent.to_field is null"}, "configuration": {"test_name": "relationships", "test_params": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": 3100, "time_utc": "2023-01-02T10:46:31+00:00", "id": "e1390d78-5bab-4d75-982c-13520970d8ae.test.elementary_integration_tests.relationships_string_column_anomalies_min_length__max_length__source_training_string_column_anomalies_training_.a2cbc353c6", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "string_column_anomalies", "column_name": "MISSING_COUNT", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:00+02:00", "latest_run_time_utc": "2023-01-02T10:44:00+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.elon_test.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "max_length", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_count__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('max_length' as varchar)\n and upper(column_name) = upper(cast('MISSING_COUNT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column MISSING_COUNT, the last max_length value is 5. The average for this metric is 5.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_count__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('max_length' as varchar)\n and upper(column_name) = upper(cast('MISSING_COUNT' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:44:00+00:00", "id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "string_column_anomalies", "column_name": "MIN_LENGTH", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:43:59+02:00", "latest_run_time_utc": "2023-01-02T10:43:59+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.elon_test.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "missing_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('missing_count' as varchar)\n and upper(column_name) = upper(cast('MIN_LENGTH' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column MIN_LENGTH, the last missing_count value is 0. The average for this metric is 0.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('missing_count' as varchar)\n and upper(column_name) = upper(cast('MIN_LENGTH' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:43:59+00:00", "id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "string_column_anomalies", "column_name": "MISSING_COUNT", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:00+02:00", "latest_run_time_utc": "2023-01-02T10:44:00+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.elon_test.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "average_length", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_count__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average_length' as varchar)\n and upper(column_name) = upper(cast('MISSING_COUNT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column MISSING_COUNT, the last average_length value is 5. The average for this metric is 5.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_count__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average_length' as varchar)\n and upper(column_name) = upper(cast('MISSING_COUNT' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:44:00+00:00", "id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "model.elementary_integration_tests.string_column_anomalies.elementary_freshness_anomalies_string_column_anomalies_.freshness_anomalies", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "string_column_anomalies", "column_name": null, "test_name": "freshness_anomalies", "test_display_name": "Freshness Anomalies", "latest_run_time": "2023-01-02T12:42:19+02:00", "latest_run_time_utc": "2023-01-02T10:42:19+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.elon_test.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "freshness", "test_query": "select * from (\n \n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_FRESHNESS_ANOMALIES_STRING_COLUMN_ANOMALIES___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n\n \n\n\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('freshness' as varchar)", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "Last update was at 1969-12-30 00:00:00.000, 48 hours ago. Usually the table is updated within 24.8 hours.", "result_query": "select * from (\n \n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_FRESHNESS_ANOMALIES_STRING_COLUMN_ANOMALIES___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n\n \n\n\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('freshness' as varchar)"}, "configuration": {"test_name": "freshness_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 3}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:41:45+00:00", "id": "1579f298-0625-44b1-9374-a1cfe7a3983c.test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "status": "fail"}, {"affected_rows": null, "time_utc": "2023-01-02T10:42:05+00:00", "id": "532358f7-914c-452d-af6f-ecabf48a4444.test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "status": "fail"}, {"affected_rows": null, "time_utc": "2023-01-02T10:42:19+00:00", "id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "status": "fail"}], "description": "There were 3 failures, no errors and no warnings on the last 3 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "string_column_anomalies", "column_name": "AVERAGE_LENGTH", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:43:59+02:00", "latest_run_time_utc": "2023-01-02T10:43:59+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.elon_test.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('AVERAGE_LENGTH' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column AVERAGE_LENGTH, the last null_count value is 0. The average for this metric is 0.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('AVERAGE_LENGTH' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:43:59+00:00", "id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_schema_changes_string_column_anomalies_.c57cd75b87", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "string_column_anomalies", "column_name": null, "test_name": "schema_changes", "test_display_name": "Schema Changes", "latest_run_time": "2023-01-02T12:46:17+02:00", "latest_run_time_utc": "2023-01-02T10:46:17+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.elon_test.string_column_anomalies", "test_type": "schema_change", "test_sub_type": "generic", "test_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.elementary_test_results\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n \n\n \n \n \n\n \n \n \n\n \n \n \n\n \n\n \n \n \n \n select * from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_SCHEMA_CHANGES_STRING_COLUMN_ANOMALIES___SCHEMA_CHANGES_ALERTS\"", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.elementary_test_results\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n \n\n \n \n \n\n \n \n \n\n \n \n \n\n \n\n \n \n \n \n select * from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_SCHEMA_CHANGES_STRING_COLUMN_ANOMALIES___SCHEMA_CHANGES_ALERTS\""}, "configuration": {"test_name": "schema_changes", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 2, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:43:14+00:00", "id": "4ea2ffbd-a9b9-46b5-85d1-64b25e74c36a.test.elementary_integration_tests.elementary_schema_changes_string_column_anomalies_.c57cd75b87", "status": "pass"}, {"affected_rows": null, "time_utc": "2023-01-02T10:46:17+00:00", "id": "6a2a0262-5ab7-44ef-af24-fdfefebe0068.test.elementary_integration_tests.elementary_schema_changes_string_column_anomalies_.c57cd75b87", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 2 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "string_column_anomalies", "column_name": "MISSING_PERCENT", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:00+02:00", "latest_run_time_utc": "2023-01-02T10:44:00+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.elon_test.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('MISSING_PERCENT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column MISSING_PERCENT, the last null_count value is 64. The average for this metric is 21.867.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('MISSING_PERCENT' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:44:00+00:00", "id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "string_column_anomalies", "column_name": "MISSING_COUNT", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:00+02:00", "latest_run_time_utc": "2023-01-02T10:44:00+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.elon_test.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "missing_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_count__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('missing_count' as varchar)\n and upper(column_name) = upper(cast('MISSING_COUNT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column MISSING_COUNT, the last missing_count value is 20. The average for this metric is 3.567.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_count__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('missing_count' as varchar)\n and upper(column_name) = upper(cast('MISSING_COUNT' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:44:00+00:00", "id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "string_column_anomalies", "column_name": "MIN_LENGTH", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:43:59+02:00", "latest_run_time_utc": "2023-01-02T10:43:59+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.elon_test.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "max_length", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('max_length' as varchar)\n and upper(column_name) = upper(cast('MIN_LENGTH' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column MIN_LENGTH, the last max_length value is 10. The average for this metric is 10.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('max_length' as varchar)\n and upper(column_name) = upper(cast('MIN_LENGTH' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:43:59+00:00", "id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "string_column_anomalies", "column_name": "MISSING_PERCENT", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:00+02:00", "latest_run_time_utc": "2023-01-02T10:44:00+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.elon_test.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "missing_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('missing_count' as varchar)\n and upper(column_name) = upper(cast('MISSING_PERCENT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column MISSING_PERCENT, the last missing_count value is 64. The average for this metric is 21.867.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('missing_count' as varchar)\n and upper(column_name) = upper(cast('MISSING_PERCENT' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:44:00+00:00", "id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "string_column_anomalies", "column_name": "MISSING_PERCENT", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:00+02:00", "latest_run_time_utc": "2023-01-02T10:44:00+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.elon_test.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('MISSING_PERCENT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column MISSING_PERCENT, the last null_percent value is 64. The average for this metric is 21.867.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('MISSING_PERCENT' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:44:00+00:00", "id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "string_column_anomalies", "column_name": "MISSING_COUNT", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:00+02:00", "latest_run_time_utc": "2023-01-02T10:44:00+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.elon_test.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_count__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('MISSING_COUNT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column MISSING_COUNT, the last null_percent value is 20. The average for this metric is 3.567.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_count__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('MISSING_COUNT' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:44:00+00:00", "id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "string_column_anomalies", "column_name": "AVERAGE_LENGTH", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:43:59+02:00", "latest_run_time_utc": "2023-01-02T10:43:59+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.elon_test.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "average_length", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average_length' as varchar)\n and upper(column_name) = upper(cast('AVERAGE_LENGTH' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column AVERAGE_LENGTH, the last average_length value is 6.44. The average for this metric is 5.048.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('average_length' as varchar)\n and upper(column_name) = upper(cast('AVERAGE_LENGTH' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:43:59+00:00", "id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "string_column_anomalies", "column_name": "MISSING_COUNT", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:00+02:00", "latest_run_time_utc": "2023-01-02T10:44:00+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.elon_test.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "min_length", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_count__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('min_length' as varchar)\n and upper(column_name) = upper(cast('MISSING_COUNT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column MISSING_COUNT, the last min_length value is 5. The average for this metric is 5.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_count__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('min_length' as varchar)\n and upper(column_name) = upper(cast('MISSING_COUNT' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:44:00+00:00", "id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "string_column_anomalies", "column_name": "MISSING_PERCENT", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:00+02:00", "latest_run_time_utc": "2023-01-02T10:44:00+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.elon_test.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "missing_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('missing_percent' as varchar)\n and upper(column_name) = upper(cast('MISSING_PERCENT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column MISSING_PERCENT, the last missing_percent value is 64. The average for this metric is 21.867.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('missing_percent' as varchar)\n and upper(column_name) = upper(cast('MISSING_PERCENT' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:44:00+00:00", "id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "string_column_anomalies", "column_name": "MISSING_COUNT", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:00+02:00", "latest_run_time_utc": "2023-01-02T10:44:00+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.elon_test.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "null_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_count__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('MISSING_COUNT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column MISSING_COUNT, the last null_count value is 20. The average for this metric is 3.567.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_count__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('MISSING_COUNT' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:44:00+00:00", "id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "string_column_anomalies", "column_name": "MISSING_PERCENT", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:44:00+02:00", "latest_run_time_utc": "2023-01-02T10:44:00+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.elon_test.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "max_length", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('max_length' as varchar)\n and upper(column_name) = upper(cast('MISSING_PERCENT' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column MISSING_PERCENT, the last max_length value is 5. The average for this metric is 5.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_missing_percent__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('max_length' as varchar)\n and upper(column_name) = upper(cast('MISSING_PERCENT' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:44:00+00:00", "id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "string_column_anomalies", "column_name": "MIN_LENGTH", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-01-02T12:43:59+02:00", "latest_run_time_utc": "2023-01-02T10:43:59+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.elon_test.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "min_length", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('min_length' as varchar)\n and upper(column_name) = upper(cast('MIN_LENGTH' as varchar))", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "In column MIN_LENGTH, the last min_length value is 1. The average for this metric is 4.867.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.STRING_COLUMN_ANOMALIES' as varchar)) and\n metric_name = cast('min_length' as varchar)\n and upper(column_name) = upper(cast('MIN_LENGTH' as varchar))"}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:43:59+00:00", "id": "e9d4a870-a67c-4133-9ae5-a6ee239bc22a.test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}], "null": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.singular_test_with_two_refs", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": null, "column_name": null, "test_name": "singular_test_with_two_refs", "test_display_name": "Singular Test With Two Refs", "latest_run_time": "2023-01-02T12:46:31+02:00", "latest_run_time_utc": "2023-01-02T10:46:31+00:00", "latest_run_status": "fail", "model_unique_id": null, "table_unique_id": "elementary_tests.elon_test", "test_type": "dbt_test", "test_sub_type": "singular", "test_query": "with min_len_issues as (\n select null_count_int as min_issue from ELEMENTARY_TESTS.elon_test.any_type_column_anomalies where null_count_int < 100\n),\n\nmin_issues as (\n select min as min_issue from ELEMENTARY_TESTS.elon_test.numeric_column_anomalies where min < 100\n),\n\nall_issues as (\n select * from min_len_issues\n union all\n select * from min_issues\n)\n\nselect * from all_issues", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "Got 95 results, configured to fail if != 0", "result_query": "with min_len_issues as (\n select null_count_int as min_issue from ELEMENTARY_TESTS.elon_test.any_type_column_anomalies where null_count_int < 100\n),\n\nmin_issues as (\n select min as min_issue from ELEMENTARY_TESTS.elon_test.numeric_column_anomalies where min < 100\n),\n\nall_issues as (\n select * from min_len_issues\n union all\n select * from min_issues\n)\n\nselect * from all_issues"}, "configuration": {"test_name": "singular_test_with_two_refs", "test_params": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": 95, "time_utc": "2023-01-02T10:46:31+00:00", "id": "e1390d78-5bab-4d75-982c-13520970d8ae.test.elementary_integration_tests.singular_test_with_two_refs", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.singular_test_with_no_ref", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": null, "column_name": null, "test_name": "singular_test_with_no_ref", "test_display_name": "Singular Test With No Ref", "latest_run_time": "2023-01-02T12:46:31+02:00", "latest_run_time_utc": "2023-01-02T10:46:31+00:00", "latest_run_status": "fail", "model_unique_id": null, "table_unique_id": "elementary_tests.elon_test", "test_type": "dbt_test", "test_sub_type": "singular", "test_query": "select min from ELEMENTARY_TESTS.elon_test.numeric_column_anomalies where min < 100", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "Got 95 results, configured to fail if != 0", "result_query": "select min from ELEMENTARY_TESTS.elon_test.numeric_column_anomalies where min < 100"}, "configuration": {"test_name": "singular_test_with_no_ref", "test_params": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": 95, "time_utc": "2023-01-02T10:46:31+00:00", "id": "e1390d78-5bab-4d75-982c-13520970d8ae.test.elementary_integration_tests.singular_test_with_no_ref", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}], "source.elementary_integration_tests.training.string_column_anomalies_training": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "string_column_anomalies_training", "column_name": null, "test_name": "freshness_anomalies", "test_display_name": "Freshness Anomalies", "latest_run_time": "2023-01-02T12:42:19+02:00", "latest_run_time_utc": "2023-01-02T10:42:19+00:00", "latest_run_status": "fail", "model_unique_id": "source.elementary_integration_tests.training.string_column_anomalies_training", "table_unique_id": "elementary_tests.elon_test.string_column_anomalies_training", "test_type": "anomaly_detection", "test_sub_type": "freshness", "test_query": "select * from (\n \n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_SOURCE_FRESHNESS_ANOMALIES_TRAINING_STRING_COLUMN_ANOMALIES_TRAINING___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n\n \n\n\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING' as varchar)) and\n metric_name = cast('freshness' as varchar)", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "Last update was at 1969-12-30 00:00:00.000, 48 hours ago. Usually the table is updated within 24.8 hours.", "result_query": "select * from (\n \n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_SOURCE_FRESHNESS_ANOMALIES_TRAINING_STRING_COLUMN_ANOMALIES_TRAINING___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n\n \n\n\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING' as varchar)) and\n metric_name = cast('freshness' as varchar)"}, "configuration": {"test_name": "freshness_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 3}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:41:45+00:00", "id": "1579f298-0625-44b1-9374-a1cfe7a3983c.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "status": "fail"}, {"affected_rows": null, "time_utc": "2023-01-02T10:42:05+00:00", "id": "532358f7-914c-452d-af6f-ecabf48a4444.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "status": "fail"}, {"affected_rows": null, "time_utc": "2023-01-02T10:42:19+00:00", "id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "status": "fail"}], "description": "There were 3 failures, no errors and no warnings on the last 3 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "string_column_anomalies_training", "column_name": null, "test_name": "volume_anomalies", "test_display_name": "Volume Anomalies", "latest_run_time": "2023-01-02T12:42:20+02:00", "latest_run_time_utc": "2023-01-02T10:42:20+00:00", "latest_run_status": "fail", "model_unique_id": "source.elementary_integration_tests.training.string_column_anomalies_training", "table_unique_id": "elementary_tests.elon_test.string_column_anomalies_training", "test_type": "anomaly_detection", "test_sub_type": "row_count", "test_query": "select * from (\n \n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_SOURCE_VOLUME_ANOMALIES_TRAINING_STRING_COLUMN_ANOMALIES_TRAINING___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n\n \n\n\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING' as varchar)) and\n metric_name = cast('row_count' as varchar)", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "The last row_count value is 0. The average for this metric is 96.667.", "result_query": "select * from (\n \n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_SOURCE_VOLUME_ANOMALIES_TRAINING_STRING_COLUMN_ANOMALIES_TRAINING___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n\n \n\n\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING' as varchar)) and\n metric_name = cast('row_count' as varchar)"}, "configuration": {"test_name": "volume_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 3}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:41:45+00:00", "id": "1579f298-0625-44b1-9374-a1cfe7a3983c.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "status": "fail"}, {"affected_rows": null, "time_utc": "2023-01-02T10:42:05+00:00", "id": "532358f7-914c-452d-af6f-ecabf48a4444.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "status": "fail"}, {"affected_rows": null, "time_utc": "2023-01-02T10:42:20+00:00", "id": "d912eb1f-67f6-44db-9d9d-acf2566de4d8.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "status": "fail"}], "description": "There were 3 failures, no errors and no warnings on the last 3 test runs."}}], "model.elementary_integration_tests.groups": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_schema_changes_from_baseline_groups_True.973df95f83", "database_name": "ELEMENTARY_TESTS", "schema_name": "ELON_TEST", "table_name": "GROUPS", "column_name": "group_d", "test_name": "schema_changes_from_baseline", "test_display_name": "Schema Changes From Baseline", "latest_run_time": "2023-01-02T12:43:23+02:00", "latest_run_time_utc": "2023-01-02T10:43:23+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.groups", "table_unique_id": "elementary_tests.elon_test.groups", "test_type": "schema_change", "test_sub_type": "column_added", "test_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n\n \n \n \n\n \n \n \n \n\n \n\n \n\n with cur as (\n \n with baseline as (\n select lower(column_name) as column_name, data_type\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_schema_changes_from_baseline_groups_true__schema_baseline__dbt_tmp\n )\n\n select\n info_schema.full_table_name,\n lower(info_schema.column_name) as column_name,\n info_schema.data_type,\n (baseline.column_name IS NULL) as is_new,\n \n cast ('2023-01-02 10:43:23' as TIMESTAMP)\n as detected_at\n from ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns info_schema\n left join baseline on (\n lower(info_schema.column_name) = lower(baseline.column_name)\n )\n where lower(info_schema.full_table_name) = lower('ELEMENTARY_TESTS.ELON_TEST.GROUPS')\n \n ),\n\n pre as (\n \n select\n cast('ELEMENTARY_TESTS.ELON_TEST.GROUPS' as varchar) as full_table_name,\n column_name,\n data_type,\n \n cast ('2023-01-02 10:43:23' as TIMESTAMP)\n as detected_at\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_schema_changes_from_baseline_groups_true__schema_baseline__dbt_tmp\n \n ),\n\n type_changes as (\n\n \n select\n cur.full_table_name,\n 'type_changed' as change,\n cur.column_name,\n cur.data_type as data_type,\n pre.data_type as pre_data_type,\n pre.detected_at\n from cur inner join pre\n on (cur.full_table_name = pre.full_table_name and cur.column_name = pre.column_name)\n where pre.data_type IS NOT NULL AND cur.data_type != pre.data_type\n\n ),\n\n \n columns_added as (\n\n \n select\n full_table_name,\n 'column_added' as change,\n column_name,\n data_type,\n \n cast(null as varchar)\n as pre_data_type,\n detected_at as detected_at\n from cur\n where is_new = true\n\n ),\n \n\n columns_removed as (\n\n \n select\n pre.full_table_name,\n 'column_removed' as change,\n pre.column_name as column_name,\n \n cast(null as varchar)\n as data_type,\n pre.data_type as pre_data_type,\n pre.detected_at as detected_at\n from pre left join cur\n on (cur.full_table_name = pre.full_table_name and cur.column_name = pre.column_name)\n where cur.full_table_name is null and cur.column_name is null\n\n ),\n\n columns_removed_filter_deleted_tables as (\n\n \n select\n removed.full_table_name,\n removed.change,\n removed.column_name,\n removed.data_type,\n removed.pre_data_type,\n removed.detected_at\n from columns_removed as removed join cur\n on (removed.full_table_name = cur.full_table_name)\n\n ),\n\n all_column_changes as (\n\n \n select * from type_changes\n union all\n select * from columns_removed_filter_deleted_tables\n \n union all\n select * from columns_added\n \n ),\n\n column_changes_test_results as (\n\n \n select\n \n \n\n md5(cast(coalesce(cast(full_table_name as varchar), '') || '-' || coalesce(cast(column_name as varchar), '') || '-' || coalesce(cast(change as varchar), '') || '-' || coalesce(cast(detected_at as varchar), '') as TEXT))\n as data_issue_id,\n \n cast ('2023-01-02 10:43:23' as TIMESTAMP)\n as detected_at,\n \n trim(split(full_table_name,'.')[0],'\"') as database_name\n\n,\n \n trim(split(full_table_name,'.')[1],'\"') as schema_name\n\n,\n \n trim(split(full_table_name,'.')[2],'\"') as table_name\n\n,\n column_name,\n 'schema_change' as test_type,\n change as test_sub_type,\n case\n when change = 'column_added'\n then 'The column \"' || column_name || '\" was added'\n when change= 'column_removed'\n then 'The column \"' || column_name || '\" was removed'\n when change= 'type_changed'\n then 'The type of \"' || column_name || '\" was changed from ' || pre_data_type || ' to ' || data_type\n else NULL\n end as test_results_description\n from all_column_changes\n group by 1,2,3,4,5,6,7,8,9\n\n )\n\n \n select \n \n\n md5(cast(coalesce(cast(data_issue_id as varchar), '') || '-' || coalesce(cast(cast('acd0caa6-5efb-4df9-b512-b449e218426f.test.elementary_integration_tests.elementary_schema_changes_from_baseline_groups_True.973df95f83' as varchar) as varchar), '') as TEXT))\n as id,\n cast('acd0caa6-5efb-4df9-b512-b449e218426f.test.elementary_integration_tests.elementary_schema_changes_from_baseline_groups_True.973df95f83' as varchar) as test_execution_id,\n cast('test.elementary_integration_tests.elementary_schema_changes_from_baseline_groups_True.973df95f83' as varchar) as test_unique_id,\n *\n from column_changes_test_results", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "The column \"group_d\" was added", "result_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n\n \n \n \n\n \n \n \n \n\n \n\n \n\n with cur as (\n \n with baseline as (\n select lower(column_name) as column_name, data_type\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_schema_changes_from_baseline_groups_true__schema_baseline__dbt_tmp\n )\n\n select\n info_schema.full_table_name,\n lower(info_schema.column_name) as column_name,\n info_schema.data_type,\n (baseline.column_name IS NULL) as is_new,\n \n cast ('2023-01-02 10:43:23' as TIMESTAMP)\n as detected_at\n from ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns info_schema\n left join baseline on (\n lower(info_schema.column_name) = lower(baseline.column_name)\n )\n where lower(info_schema.full_table_name) = lower('ELEMENTARY_TESTS.ELON_TEST.GROUPS')\n \n ),\n\n pre as (\n \n select\n cast('ELEMENTARY_TESTS.ELON_TEST.GROUPS' as varchar) as full_table_name,\n column_name,\n data_type,\n \n cast ('2023-01-02 10:43:23' as TIMESTAMP)\n as detected_at\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_schema_changes_from_baseline_groups_true__schema_baseline__dbt_tmp\n \n ),\n\n type_changes as (\n\n \n select\n cur.full_table_name,\n 'type_changed' as change,\n cur.column_name,\n cur.data_type as data_type,\n pre.data_type as pre_data_type,\n pre.detected_at\n from cur inner join pre\n on (cur.full_table_name = pre.full_table_name and cur.column_name = pre.column_name)\n where pre.data_type IS NOT NULL AND cur.data_type != pre.data_type\n\n ),\n\n \n columns_added as (\n\n \n select\n full_table_name,\n 'column_added' as change,\n column_name,\n data_type,\n \n cast(null as varchar)\n as pre_data_type,\n detected_at as detected_at\n from cur\n where is_new = true\n\n ),\n \n\n columns_removed as (\n\n \n select\n pre.full_table_name,\n 'column_removed' as change,\n pre.column_name as column_name,\n \n cast(null as varchar)\n as data_type,\n pre.data_type as pre_data_type,\n pre.detected_at as detected_at\n from pre left join cur\n on (cur.full_table_name = pre.full_table_name and cur.column_name = pre.column_name)\n where cur.full_table_name is null and cur.column_name is null\n\n ),\n\n columns_removed_filter_deleted_tables as (\n\n \n select\n removed.full_table_name,\n removed.change,\n removed.column_name,\n removed.data_type,\n removed.pre_data_type,\n removed.detected_at\n from columns_removed as removed join cur\n on (removed.full_table_name = cur.full_table_name)\n\n ),\n\n all_column_changes as (\n\n \n select * from type_changes\n union all\n select * from columns_removed_filter_deleted_tables\n \n union all\n select * from columns_added\n \n ),\n\n column_changes_test_results as (\n\n \n select\n \n \n\n md5(cast(coalesce(cast(full_table_name as varchar), '') || '-' || coalesce(cast(column_name as varchar), '') || '-' || coalesce(cast(change as varchar), '') || '-' || coalesce(cast(detected_at as varchar), '') as TEXT))\n as data_issue_id,\n \n cast ('2023-01-02 10:43:23' as TIMESTAMP)\n as detected_at,\n \n trim(split(full_table_name,'.')[0],'\"') as database_name\n\n,\n \n trim(split(full_table_name,'.')[1],'\"') as schema_name\n\n,\n \n trim(split(full_table_name,'.')[2],'\"') as table_name\n\n,\n column_name,\n 'schema_change' as test_type,\n change as test_sub_type,\n case\n when change = 'column_added'\n then 'The column \"' || column_name || '\" was added'\n when change= 'column_removed'\n then 'The column \"' || column_name || '\" was removed'\n when change= 'type_changed'\n then 'The type of \"' || column_name || '\" was changed from ' || pre_data_type || ' to ' || data_type\n else NULL\n end as test_results_description\n from all_column_changes\n group by 1,2,3,4,5,6,7,8,9\n\n )\n\n \n select \n \n\n md5(cast(coalesce(cast(data_issue_id as varchar), '') || '-' || coalesce(cast(cast('acd0caa6-5efb-4df9-b512-b449e218426f.test.elementary_integration_tests.elementary_schema_changes_from_baseline_groups_True.973df95f83' as varchar) as varchar), '') as TEXT))\n as id,\n cast('acd0caa6-5efb-4df9-b512-b449e218426f.test.elementary_integration_tests.elementary_schema_changes_from_baseline_groups_True.973df95f83' as varchar) as test_execution_id,\n cast('test.elementary_integration_tests.elementary_schema_changes_from_baseline_groups_True.973df95f83' as varchar) as test_unique_id,\n *\n from column_changes_test_results"}, "configuration": {"test_name": "schema_changes_from_baseline", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:43:23+00:00", "id": "acd0caa6-5efb-4df9-b512-b449e218426f.test.elementary_integration_tests.elementary_schema_changes_from_baseline_groups_True.973df95f83", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_schema_changes_groups_.b321d3c7ba", "database_name": "ELEMENTARY_TESTS", "schema_name": "ELON_TEST", "table_name": "GROUPS", "column_name": "GROUP_A", "test_name": "schema_changes", "test_display_name": "Schema Changes", "latest_run_time": "2023-01-02T12:46:13+02:00", "latest_run_time_utc": "2023-01-02T10:46:13+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.groups", "table_unique_id": "elementary_tests.elon_test.groups", "test_type": "schema_change", "test_sub_type": "column_removed", "test_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.elementary_test_results\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n \n\n \n \n \n\n \n \n \n\n \n \n \n\n \n\n \n \n \n \n select * from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_SCHEMA_CHANGES_GROUPS___SCHEMA_CHANGES_ALERTS\"", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "The column \"GROUP_A\" was removed", "result_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.elementary_test_results\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n \n\n \n \n \n\n \n \n \n\n \n \n \n\n \n\n \n \n \n \n select * from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_SCHEMA_CHANGES_GROUPS___SCHEMA_CHANGES_ALERTS\""}, "configuration": {"test_name": "schema_changes", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:46:13+00:00", "id": "6a2a0262-5ab7-44ef-af24-fdfefebe0068.test.elementary_integration_tests.elementary_schema_changes_groups_.b321d3c7ba", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_schema_changes_from_baseline_groups_True.973df95f83", "database_name": "ELEMENTARY_TESTS", "schema_name": "ELON_TEST", "table_name": "GROUPS", "column_name": "group_b", "test_name": "schema_changes_from_baseline", "test_display_name": "Schema Changes From Baseline", "latest_run_time": "2023-01-02T12:43:23+02:00", "latest_run_time_utc": "2023-01-02T10:43:23+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.groups", "table_unique_id": "elementary_tests.elon_test.groups", "test_type": "schema_change", "test_sub_type": "type_changed", "test_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n\n \n \n \n\n \n \n \n \n\n \n\n \n\n with cur as (\n \n with baseline as (\n select lower(column_name) as column_name, data_type\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_schema_changes_from_baseline_groups_true__schema_baseline__dbt_tmp\n )\n\n select\n info_schema.full_table_name,\n lower(info_schema.column_name) as column_name,\n info_schema.data_type,\n (baseline.column_name IS NULL) as is_new,\n \n cast ('2023-01-02 10:43:23' as TIMESTAMP)\n as detected_at\n from ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns info_schema\n left join baseline on (\n lower(info_schema.column_name) = lower(baseline.column_name)\n )\n where lower(info_schema.full_table_name) = lower('ELEMENTARY_TESTS.ELON_TEST.GROUPS')\n \n ),\n\n pre as (\n \n select\n cast('ELEMENTARY_TESTS.ELON_TEST.GROUPS' as varchar) as full_table_name,\n column_name,\n data_type,\n \n cast ('2023-01-02 10:43:23' as TIMESTAMP)\n as detected_at\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_schema_changes_from_baseline_groups_true__schema_baseline__dbt_tmp\n \n ),\n\n type_changes as (\n\n \n select\n cur.full_table_name,\n 'type_changed' as change,\n cur.column_name,\n cur.data_type as data_type,\n pre.data_type as pre_data_type,\n pre.detected_at\n from cur inner join pre\n on (cur.full_table_name = pre.full_table_name and cur.column_name = pre.column_name)\n where pre.data_type IS NOT NULL AND cur.data_type != pre.data_type\n\n ),\n\n \n columns_added as (\n\n \n select\n full_table_name,\n 'column_added' as change,\n column_name,\n data_type,\n \n cast(null as varchar)\n as pre_data_type,\n detected_at as detected_at\n from cur\n where is_new = true\n\n ),\n \n\n columns_removed as (\n\n \n select\n pre.full_table_name,\n 'column_removed' as change,\n pre.column_name as column_name,\n \n cast(null as varchar)\n as data_type,\n pre.data_type as pre_data_type,\n pre.detected_at as detected_at\n from pre left join cur\n on (cur.full_table_name = pre.full_table_name and cur.column_name = pre.column_name)\n where cur.full_table_name is null and cur.column_name is null\n\n ),\n\n columns_removed_filter_deleted_tables as (\n\n \n select\n removed.full_table_name,\n removed.change,\n removed.column_name,\n removed.data_type,\n removed.pre_data_type,\n removed.detected_at\n from columns_removed as removed join cur\n on (removed.full_table_name = cur.full_table_name)\n\n ),\n\n all_column_changes as (\n\n \n select * from type_changes\n union all\n select * from columns_removed_filter_deleted_tables\n \n union all\n select * from columns_added\n \n ),\n\n column_changes_test_results as (\n\n \n select\n \n \n\n md5(cast(coalesce(cast(full_table_name as varchar), '') || '-' || coalesce(cast(column_name as varchar), '') || '-' || coalesce(cast(change as varchar), '') || '-' || coalesce(cast(detected_at as varchar), '') as TEXT))\n as data_issue_id,\n \n cast ('2023-01-02 10:43:23' as TIMESTAMP)\n as detected_at,\n \n trim(split(full_table_name,'.')[0],'\"') as database_name\n\n,\n \n trim(split(full_table_name,'.')[1],'\"') as schema_name\n\n,\n \n trim(split(full_table_name,'.')[2],'\"') as table_name\n\n,\n column_name,\n 'schema_change' as test_type,\n change as test_sub_type,\n case\n when change = 'column_added'\n then 'The column \"' || column_name || '\" was added'\n when change= 'column_removed'\n then 'The column \"' || column_name || '\" was removed'\n when change= 'type_changed'\n then 'The type of \"' || column_name || '\" was changed from ' || pre_data_type || ' to ' || data_type\n else NULL\n end as test_results_description\n from all_column_changes\n group by 1,2,3,4,5,6,7,8,9\n\n )\n\n \n select \n \n\n md5(cast(coalesce(cast(data_issue_id as varchar), '') || '-' || coalesce(cast(cast('acd0caa6-5efb-4df9-b512-b449e218426f.test.elementary_integration_tests.elementary_schema_changes_from_baseline_groups_True.973df95f83' as varchar) as varchar), '') as TEXT))\n as id,\n cast('acd0caa6-5efb-4df9-b512-b449e218426f.test.elementary_integration_tests.elementary_schema_changes_from_baseline_groups_True.973df95f83' as varchar) as test_execution_id,\n cast('test.elementary_integration_tests.elementary_schema_changes_from_baseline_groups_True.973df95f83' as varchar) as test_unique_id,\n *\n from column_changes_test_results", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "The type of \"group_b\" was changed from DOUBLE to TEXT", "result_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n\n \n \n \n\n \n \n \n \n\n \n\n \n\n with cur as (\n \n with baseline as (\n select lower(column_name) as column_name, data_type\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_schema_changes_from_baseline_groups_true__schema_baseline__dbt_tmp\n )\n\n select\n info_schema.full_table_name,\n lower(info_schema.column_name) as column_name,\n info_schema.data_type,\n (baseline.column_name IS NULL) as is_new,\n \n cast ('2023-01-02 10:43:23' as TIMESTAMP)\n as detected_at\n from ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns info_schema\n left join baseline on (\n lower(info_schema.column_name) = lower(baseline.column_name)\n )\n where lower(info_schema.full_table_name) = lower('ELEMENTARY_TESTS.ELON_TEST.GROUPS')\n \n ),\n\n pre as (\n \n select\n cast('ELEMENTARY_TESTS.ELON_TEST.GROUPS' as varchar) as full_table_name,\n column_name,\n data_type,\n \n cast ('2023-01-02 10:43:23' as TIMESTAMP)\n as detected_at\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_schema_changes_from_baseline_groups_true__schema_baseline__dbt_tmp\n \n ),\n\n type_changes as (\n\n \n select\n cur.full_table_name,\n 'type_changed' as change,\n cur.column_name,\n cur.data_type as data_type,\n pre.data_type as pre_data_type,\n pre.detected_at\n from cur inner join pre\n on (cur.full_table_name = pre.full_table_name and cur.column_name = pre.column_name)\n where pre.data_type IS NOT NULL AND cur.data_type != pre.data_type\n\n ),\n\n \n columns_added as (\n\n \n select\n full_table_name,\n 'column_added' as change,\n column_name,\n data_type,\n \n cast(null as varchar)\n as pre_data_type,\n detected_at as detected_at\n from cur\n where is_new = true\n\n ),\n \n\n columns_removed as (\n\n \n select\n pre.full_table_name,\n 'column_removed' as change,\n pre.column_name as column_name,\n \n cast(null as varchar)\n as data_type,\n pre.data_type as pre_data_type,\n pre.detected_at as detected_at\n from pre left join cur\n on (cur.full_table_name = pre.full_table_name and cur.column_name = pre.column_name)\n where cur.full_table_name is null and cur.column_name is null\n\n ),\n\n columns_removed_filter_deleted_tables as (\n\n \n select\n removed.full_table_name,\n removed.change,\n removed.column_name,\n removed.data_type,\n removed.pre_data_type,\n removed.detected_at\n from columns_removed as removed join cur\n on (removed.full_table_name = cur.full_table_name)\n\n ),\n\n all_column_changes as (\n\n \n select * from type_changes\n union all\n select * from columns_removed_filter_deleted_tables\n \n union all\n select * from columns_added\n \n ),\n\n column_changes_test_results as (\n\n \n select\n \n \n\n md5(cast(coalesce(cast(full_table_name as varchar), '') || '-' || coalesce(cast(column_name as varchar), '') || '-' || coalesce(cast(change as varchar), '') || '-' || coalesce(cast(detected_at as varchar), '') as TEXT))\n as data_issue_id,\n \n cast ('2023-01-02 10:43:23' as TIMESTAMP)\n as detected_at,\n \n trim(split(full_table_name,'.')[0],'\"') as database_name\n\n,\n \n trim(split(full_table_name,'.')[1],'\"') as schema_name\n\n,\n \n trim(split(full_table_name,'.')[2],'\"') as table_name\n\n,\n column_name,\n 'schema_change' as test_type,\n change as test_sub_type,\n case\n when change = 'column_added'\n then 'The column \"' || column_name || '\" was added'\n when change= 'column_removed'\n then 'The column \"' || column_name || '\" was removed'\n when change= 'type_changed'\n then 'The type of \"' || column_name || '\" was changed from ' || pre_data_type || ' to ' || data_type\n else NULL\n end as test_results_description\n from all_column_changes\n group by 1,2,3,4,5,6,7,8,9\n\n )\n\n \n select \n \n\n md5(cast(coalesce(cast(data_issue_id as varchar), '') || '-' || coalesce(cast(cast('acd0caa6-5efb-4df9-b512-b449e218426f.test.elementary_integration_tests.elementary_schema_changes_from_baseline_groups_True.973df95f83' as varchar) as varchar), '') as TEXT))\n as id,\n cast('acd0caa6-5efb-4df9-b512-b449e218426f.test.elementary_integration_tests.elementary_schema_changes_from_baseline_groups_True.973df95f83' as varchar) as test_execution_id,\n cast('test.elementary_integration_tests.elementary_schema_changes_from_baseline_groups_True.973df95f83' as varchar) as test_unique_id,\n *\n from column_changes_test_results"}, "configuration": {"test_name": "schema_changes_from_baseline", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:43:23+00:00", "id": "acd0caa6-5efb-4df9-b512-b449e218426f.test.elementary_integration_tests.elementary_schema_changes_from_baseline_groups_True.973df95f83", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_schema_changes_groups_.b321d3c7ba", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "groups", "column_name": null, "test_name": "schema_changes", "test_display_name": "Schema Changes", "latest_run_time": "2023-01-02T12:43:14+02:00", "latest_run_time_utc": "2023-01-02T10:43:14+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.groups", "table_unique_id": "elementary_tests.elon_test.groups", "test_type": "schema_change", "test_sub_type": "generic", "test_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.elementary_test_results\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n \n\n \n \n \n\n \n \n \n\n \n \n \n\n \n\n \n \n \n \n select * from ELEMENTARY_TESTS.elon_test_elementary.elementary_schema_changes_groups___schema_changes_alerts", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.elementary_test_results\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n \n\n \n \n \n\n \n \n \n\n \n \n \n\n \n\n \n \n \n \n select * from ELEMENTARY_TESTS.elon_test_elementary.elementary_schema_changes_groups___schema_changes_alerts"}, "configuration": {"test_name": "schema_changes", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:43:14+00:00", "id": "4ea2ffbd-a9b9-46b5-85d1-64b25e74c36a.test.elementary_integration_tests.elementary_schema_changes_groups_.b321d3c7ba", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}], "model.elementary_integration_tests.error_model": [{"metadata": {"test_unique_id": "model.elementary_integration_tests.error_model.missing_column.uniques_error_model_missing_column.uniques", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "error_model", "column_name": "missing_column", "test_name": "uniques", "test_display_name": "Uniques", "latest_run_time": "2023-01-02T12:46:34+02:00", "latest_run_time_utc": "2023-01-02T10:46:34+00:00", "latest_run_status": "error", "model_unique_id": "model.elementary_integration_tests.error_model", "table_unique_id": "elementary_tests.elon_test.error_model", "test_type": "dbt_test", "test_sub_type": "generic", "test_query": null, "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "Compilation Error in test uniques_error_model_missing_column (models/schema.yml)\n 'test_uniques' is undefined. This can happen when calling a macro that does not exist. Check for typos and/or install package dependencies with \"dbt deps\".", "result_query": null}, "configuration": {"test_name": "uniques", "test_params": null}}, "test_runs": {"fail_rate": 1.0, "totals": {"errors": 2, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:42:33+00:00", "id": "4904fd77-a13a-4046-a399-b204060873df.test.elementary_integration_tests.uniques_error_model_missing_column.3700cec9df", "status": "error"}, {"affected_rows": null, "time_utc": "2023-01-02T10:46:34+00:00", "id": "e1390d78-5bab-4d75-982c-13520970d8ae.test.elementary_integration_tests.uniques_error_model_missing_column.3700cec9df", "status": "error"}], "description": "There were no failures, 2 errors and no warnings on the last 2 test runs."}}], "model.elementary_integration_tests.dimension_anomalies": [{"metadata": {"test_unique_id": "model.elementary_integration_tests.dimension_anomalies.elementary_dimension_anomalies_dimension_anomalies_platform.dimension_anomalies", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "dimension_anomalies", "column_name": null, "test_name": "dimension_anomalies", "test_display_name": "Dimension Anomalies", "latest_run_time": "2023-01-02T12:45:53+02:00", "latest_run_time_utc": "2023-01-02T10:45:53+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.dimension_anomalies", "table_unique_id": "elementary_tests.elon_test.dimension_anomalies", "test_type": "anomaly_detection", "test_sub_type": "dimension", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n\n \n \n \n \n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n select * from (with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_dimension_anomalies_dimension_anomalies_platform__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value)\n where is_anomalous = true\n\n \n\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.DIMENSION_ANOMALIES' as varchar)) and\n metric_name = cast('dimension' as varchar)", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "The last dimension value for dimension platform - ios is 1. The average for this metric is 19.367.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n\n \n \n \n \n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n select * from (with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_dimension_anomalies_dimension_anomalies_platform__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value)\n where is_anomalous = true\n\n \n\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.DIMENSION_ANOMALIES' as varchar)) and\n metric_name = cast('dimension' as varchar)"}, "configuration": {"test_name": "dimension_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:53+00:00", "id": "b99c5b6d-11ba-4a1d-8202-fe9bac2fd0bd.test.elementary_integration_tests.elementary_dimension_anomalies_dimension_anomalies_platform.cf343e4b29", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_dimension_anomalies_dimension_anomalies_platform__version.b65d46fbf9", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "dimension_anomalies", "column_name": null, "test_name": "dimension_anomalies", "test_display_name": "Dimension Anomalies", "latest_run_time": "2023-01-02T12:45:53+02:00", "latest_run_time_utc": "2023-01-02T10:45:53+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.dimension_anomalies", "table_unique_id": "elementary_tests.elon_test.dimension_anomalies", "test_type": "anomaly_detection", "test_sub_type": "dimension", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n\n \n \n \n \n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n select * from (with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_dimension_anomalies_dimension_anomalies_platform__version__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value)\n where is_anomalous = true\n\n \n\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.DIMENSION_ANOMALIES' as varchar)) and\n metric_name = cast('dimension' as varchar)", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "The last dimension value for dimension platform; version - ios; 2 is 0. The average for this metric is 6.767.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n\n \n \n \n \n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n select * from (with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_dimension_anomalies_dimension_anomalies_platform__version__anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value)\n where is_anomalous = true\n\n \n\n)\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.ELON_TEST.DIMENSION_ANOMALIES' as varchar)) and\n metric_name = cast('dimension' as varchar)"}, "configuration": {"test_name": "dimension_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:53+00:00", "id": "b99c5b6d-11ba-4a1d-8202-fe9bac2fd0bd.test.elementary_integration_tests.elementary_dimension_anomalies_dimension_anomalies_platform__version.b65d46fbf9", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_dimension_anomalies_dimension_anomalies_platform__platform_android_.89f503656a", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "dimension_anomalies", "column_name": null, "test_name": "dimension_anomalies", "test_display_name": "Dimension Anomalies", "latest_run_time": "2023-01-02T12:45:56+02:00", "latest_run_time_utc": "2023-01-02T10:45:56+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.dimension_anomalies", "table_unique_id": "elementary_tests.elon_test.dimension_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n\n \n \n \n \n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n select * from (with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_dimension_anomalies_dimension_anomalies_platform__platform_android___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value)\n where is_anomalous = true", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.dbt_run_results\n \n \n \n \n\n \n \n \n \n\n \n \n\n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n\n \n \n \n \n\n \n \n \n\n \n \n \n \n\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n\n\n select * from (with anomaly_scores as (\n select\n *,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(metric_value/3600,2)) || ' hours ago. Usually the table is updated within ' || abs(round(training_avg/3600,2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(metric_value,3) ||\n '. The average for this metric is ' || round(training_avg,3) || '.'\n\n else null\n end as anomaly_description\n\n from ELEMENTARY_TESTS.elon_test_elementary.elementary_dimension_anomalies_dimension_anomalies_platform__platform_android___anomaly_scores\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n abs(anomaly_score) > 3 and\n bucket_end >= \n dateadd(day, '-2', cast('1970-01-01 00:00:00' as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n case when is_anomalous = TRUE then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else min_metric_value end as min_value,\n case when is_anomalous = TRUE then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value)\n where is_anomalous = true"}, "configuration": {"test_name": "dimension_anomalies", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:45:56+00:00", "id": "b99c5b6d-11ba-4a1d-8202-fe9bac2fd0bd.test.elementary_integration_tests.elementary_dimension_anomalies_dimension_anomalies_platform__platform_android_.89f503656a", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}], "source.elementary_integration_tests.validation.any_type_column_anomalies_validation": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.source_generic_test_on_column_validation_any_type_column_anomalies_validation_null_count_int.13c361724d", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "any_type_column_anomalies_validation", "column_name": "null_count_int", "test_name": "generic_test_on_column", "test_display_name": "Generic Test On Column", "latest_run_time": "2023-01-02T12:46:31+02:00", "latest_run_time_utc": "2023-01-02T10:46:31+00:00", "latest_run_status": "fail", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.elon_test.any_type_column_anomalies_validation", "test_type": "dbt_test", "test_sub_type": "generic", "test_query": "with nothing as (select 1 as num)\n select * from nothing where num = 1", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "Got 1 result, configured to fail if != 0", "result_query": "with nothing as (select 1 as num)\n select * from nothing where num = 1"}, "configuration": {"test_name": "generic_test_on_column", "test_params": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": 1, "time_utc": "2023-01-02T10:46:31+00:00", "id": "e1390d78-5bab-4d75-982c-13520970d8ae.test.elementary_integration_tests.source_generic_test_on_column_validation_any_type_column_anomalies_validation_null_count_int.13c361724d", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}], "model.elementary_integration_tests.stats_team": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_schema_changes_stats_team_.fe2147cc27", "database_name": "ELEMENTARY_TESTS", "schema_name": "elon_test", "table_name": "stats_team", "column_name": null, "test_name": "schema_changes", "test_display_name": "Schema Changes", "latest_run_time": "2023-01-02T12:43:14+02:00", "latest_run_time_utc": "2023-01-02T10:43:14+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.stats_team", "table_unique_id": "elementary_tests.elon_test.stats_team", "test_type": "schema_change", "test_sub_type": "generic", "test_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.elementary_test_results\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n \n\n \n \n \n\n \n \n \n\n \n \n \n\n \n\n \n \n \n \n select * from ELEMENTARY_TESTS.elon_test_elementary.elementary_schema_changes_stats_team___schema_changes_alerts", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.elementary_test_results\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n \n\n \n \n \n\n \n \n \n\n \n \n \n\n \n\n \n \n \n \n select * from ELEMENTARY_TESTS.elon_test_elementary.elementary_schema_changes_stats_team___schema_changes_alerts"}, "configuration": {"test_name": "schema_changes", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:43:14+00:00", "id": "4ea2ffbd-a9b9-46b5-85d1-64b25e74c36a.test.elementary_integration_tests.elementary_schema_changes_stats_team_.fe2147cc27", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_schema_changes_stats_team_.fe2147cc27", "database_name": "ELEMENTARY_TESTS", "schema_name": "ELON_TEST", "table_name": "STATS_TEAM", "column_name": "GOALS", "test_name": "schema_changes", "test_display_name": "Schema Changes", "latest_run_time": "2023-01-02T12:46:14+02:00", "latest_run_time_utc": "2023-01-02T10:46:14+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.stats_team", "table_unique_id": "elementary_tests.elon_test.stats_team", "test_type": "schema_change", "test_sub_type": "type_changed", "test_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.elementary_test_results\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n \n\n \n \n \n\n \n \n \n\n \n \n \n\n \n\n \n \n \n \n select * from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_SCHEMA_CHANGES_STATS_TEAM___SCHEMA_CHANGES_ALERTS\"", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "The type of \"GOALS\" was changed from NUMBER to TEXT", "result_query": "-- depends_on: ELEMENTARY_TESTS.elon_test_elementary.elementary_test_results\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.schema_columns_snapshot\n -- depends_on: ELEMENTARY_TESTS.elon_test_elementary.filtered_information_schema_columns\n \n \n \n \n\n \n \n \n\n \n \n \n\n \n \n \n\n \n\n \n \n \n \n select * from \"ELEMENTARY_TESTS\".\"ELON_TEST_ELEMENTARY\".\"ELEMENTARY_SCHEMA_CHANGES_STATS_TEAM___SCHEMA_CHANGES_ALERTS\""}, "configuration": {"test_name": "schema_changes", "timestamp_column": null, "testing_timeframe": null, "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-01-02T10:46:14+00:00", "id": "6a2a0262-5ab7-44ef-af24-fdfefebe0068.test.elementary_integration_tests.elementary_schema_changes_stats_team_.fe2147cc27", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}]}, "test_runs_totals": {"model.elementary_integration_tests.numeric_column_anomalies": {"errors": 0, "warnings": 0, "passed": 10, "failures": 22}, "model.elementary_integration_tests.any_type_column_anomalies": {"errors": 0, "warnings": 3, "passed": 17, "failures": 40}, "model.elementary_integration_tests.stats_players": {"errors": 0, "warnings": 0, "passed": 1, "failures": 5}, "source.elementary_integration_tests.training.numeric_column_anomalies_training": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "source.elementary_integration_tests.training.any_type_column_anomalies_training": {"errors": 0, "warnings": 0, "passed": 6, "failures": 0}, "model.elementary_integration_tests.string_column_anomalies": {"errors": 0, "warnings": 0, "passed": 11, "failures": 14}, "null": {"errors": 0, "warnings": 0, "passed": 0, "failures": 2}, "source.elementary_integration_tests.training.string_column_anomalies_training": {"errors": 0, "warnings": 0, "passed": 0, "failures": 6}, "model.elementary_integration_tests.groups": {"errors": 0, "warnings": 0, "passed": 1, "failures": 3}, "model.elementary_integration_tests.error_model": {"errors": 2, "warnings": 0, "passed": 0, "failures": 0}, "model.elementary_integration_tests.dimension_anomalies": {"errors": 0, "warnings": 0, "passed": 1, "failures": 2}, "source.elementary_integration_tests.validation.any_type_column_anomalies_validation": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "model.elementary_integration_tests.stats_team": {"errors": 0, "warnings": 0, "passed": 1, "failures": 1}}, "coverages": {"source.elementary_integration_tests.training.numeric_column_anomalies_training": {"table_tests": 1, "column_tests": 0}, "model.elementary_integration_tests.numeric_column_anomalies": {"table_tests": 5, "column_tests": 14}, "null": {"table_tests": 2, "column_tests": 0}, "model.elementary_integration_tests.any_type_column_anomalies": {"table_tests": 3, "column_tests": 0}, "model.elementary_integration_tests.no_timestamp_anomalies": {"table_tests": 1, "column_tests": 1}, "model.elementary_integration_tests.dimension_anomalies": {"table_tests": 3, "column_tests": 0}, "model.elementary_integration_tests.error_model": {"table_tests": 0, "column_tests": 1}, "model.elementary_integration_tests.string_column_anomalies": {"table_tests": 3, "column_tests": 7}, "model.elementary_integration_tests.stats_players": {"table_tests": 2, "column_tests": 0}, "source.elementary_integration_tests.training.any_type_column_anomalies_training": {"table_tests": 2, "column_tests": 0}, "source.elementary_integration_tests.training.string_column_anomalies_training": {"table_tests": 2, "column_tests": 0}, "source.elementary_integration_tests.validation.any_type_column_anomalies_validation": {"table_tests": 1, "column_tests": 1}, "model.elementary_integration_tests.groups": {"table_tests": 2, "column_tests": 0}, "model.elementary_integration_tests.stats_team": {"table_tests": 1, "column_tests": 0}}, "model_runs": [{"unique_id": "model.elementary_integration_tests.error_model", "schema": "elon_test", "name": "error_model", "status": "error", "last_exec_time": 0.0, "median_exec_time": 0.0, "exec_time_change_rate": 0.0, "totals": {"errors": 3, "success": 0}, "runs": [{"id": "0252f707-dfc4-4aeb-b95d-450915338ae9", "time_utc": "2023-01-02T10:41:32+00:00", "status": "error", "full_refresh": false, "materialization": "view", "execution_time": 0.0}, {"id": "ba672d6c-881c-4d48-9635-7404df052f23", "time_utc": "2023-01-02T10:42:44+00:00", "status": "error", "full_refresh": false, "materialization": "view", "execution_time": 0.0}, {"id": "69e51fb5-f09e-4cc1-9286-d177457e16f8", "time_utc": "2023-01-02T10:43:46+00:00", "status": "error", "full_refresh": false, "materialization": "view", "execution_time": 0.0}]}, {"unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "schema": "elon_test", "name": "any_type_column_anomalies", "status": "success", "last_exec_time": 1.5, "median_exec_time": 1.4, "exec_time_change_rate": 7.14285714285714, "totals": {"errors": 0, "success": 2}, "runs": [{"id": "0252f707-dfc4-4aeb-b95d-450915338ae9", "time_utc": "2023-01-02T10:41:32+00:00", "status": "success", "full_refresh": false, "materialization": "view", "execution_time": 1.3}, {"id": "69e51fb5-f09e-4cc1-9286-d177457e16f8", "time_utc": "2023-01-02T10:43:46+00:00", "status": "success", "full_refresh": false, "materialization": "view", "execution_time": 1.5}]}, {"unique_id": "model.elementary_integration_tests.no_timestamp_anomalies", "schema": "elon_test", "name": "no_timestamp_anomalies", "status": "success", "last_exec_time": 1.6, "median_exec_time": 1.5, "exec_time_change_rate": 6.666666666666665, "totals": {"errors": 0, "success": 2}, "runs": [{"id": "0252f707-dfc4-4aeb-b95d-450915338ae9", "time_utc": "2023-01-02T10:41:32+00:00", "status": "success", "full_refresh": false, "materialization": "view", "execution_time": 1.4}, {"id": "69e51fb5-f09e-4cc1-9286-d177457e16f8", "time_utc": "2023-01-02T10:43:46+00:00", "status": "success", "full_refresh": false, "materialization": "view", "execution_time": 1.6}]}, {"unique_id": "model.elementary_integration_tests.groups", "schema": "elon_test", "name": "groups", "status": "success", "last_exec_time": 1.5, "median_exec_time": 1.75, "exec_time_change_rate": -14.28571428571429, "totals": {"errors": 0, "success": 2}, "runs": [{"id": "0252f707-dfc4-4aeb-b95d-450915338ae9", "time_utc": "2023-01-02T10:41:32+00:00", "status": "success", "full_refresh": false, "materialization": "view", "execution_time": 2.0}, {"id": "69e51fb5-f09e-4cc1-9286-d177457e16f8", "time_utc": "2023-01-02T10:43:46+00:00", "status": "success", "full_refresh": false, "materialization": "view", "execution_time": 1.5}]}, {"unique_id": "model.elementary_integration_tests.nested", "schema": "elon_test", "name": "nested", "status": "success", "last_exec_time": 1.3, "median_exec_time": 1.7000000000000002, "exec_time_change_rate": -23.529411764705888, "totals": {"errors": 0, "success": 2}, "runs": [{"id": "0252f707-dfc4-4aeb-b95d-450915338ae9", "time_utc": "2023-01-02T10:41:32+00:00", "status": "success", "full_refresh": false, "materialization": "view", "execution_time": 2.1}, {"id": "69e51fb5-f09e-4cc1-9286-d177457e16f8", "time_utc": "2023-01-02T10:43:46+00:00", "status": "success", "full_refresh": false, "materialization": "view", "execution_time": 1.3}]}, {"unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "schema": "elon_test", "name": "numeric_column_anomalies", "status": "success", "last_exec_time": 1.4, "median_exec_time": 1.75, "exec_time_change_rate": -20.000000000000007, "totals": {"errors": 0, "success": 2}, "runs": [{"id": "0252f707-dfc4-4aeb-b95d-450915338ae9", "time_utc": "2023-01-02T10:41:32+00:00", "status": "success", "full_refresh": false, "materialization": "view", "execution_time": 2.1}, {"id": "69e51fb5-f09e-4cc1-9286-d177457e16f8", "time_utc": "2023-01-02T10:43:46+00:00", "status": "success", "full_refresh": false, "materialization": "view", "execution_time": 1.4}]}, {"unique_id": "model.elementary_integration_tests.string_column_anomalies", "schema": "elon_test", "name": "string_column_anomalies", "status": "success", "last_exec_time": 2.1, "median_exec_time": 2.1, "exec_time_change_rate": 0.0, "totals": {"errors": 0, "success": 2}, "runs": [{"id": "0252f707-dfc4-4aeb-b95d-450915338ae9", "time_utc": "2023-01-02T10:41:32+00:00", "status": "success", "full_refresh": false, "materialization": "view", "execution_time": 2.1}, {"id": "69e51fb5-f09e-4cc1-9286-d177457e16f8", "time_utc": "2023-01-02T10:43:46+00:00", "status": "success", "full_refresh": false, "materialization": "view", "execution_time": 2.1}]}, {"unique_id": "model.elementary_integration_tests.stats_players", "schema": "elon_test", "name": "stats_players", "status": "success", "last_exec_time": 1.4, "median_exec_time": 1.75, "exec_time_change_rate": -20.000000000000007, "totals": {"errors": 0, "success": 2}, "runs": [{"id": "0252f707-dfc4-4aeb-b95d-450915338ae9", "time_utc": "2023-01-02T10:41:32+00:00", "status": "success", "full_refresh": false, "materialization": "view", "execution_time": 2.1}, {"id": "69e51fb5-f09e-4cc1-9286-d177457e16f8", "time_utc": "2023-01-02T10:43:46+00:00", "status": "success", "full_refresh": false, "materialization": "view", "execution_time": 1.4}]}, {"unique_id": "model.elementary_integration_tests.stats_team", "schema": "elon_test", "name": "stats_team", "status": "success", "last_exec_time": 1.4, "median_exec_time": 2.0, "exec_time_change_rate": -30.000000000000004, "totals": {"errors": 0, "success": 2}, "runs": [{"id": "0252f707-dfc4-4aeb-b95d-450915338ae9", "time_utc": "2023-01-02T10:41:32+00:00", "status": "success", "full_refresh": false, "materialization": "view", "execution_time": 2.6}, {"id": "69e51fb5-f09e-4cc1-9286-d177457e16f8", "time_utc": "2023-01-02T10:43:46+00:00", "status": "success", "full_refresh": false, "materialization": "view", "execution_time": 1.4}]}, {"unique_id": "model.elementary_integration_tests.dimension_anomalies", "schema": "elon_test", "name": "dimension_anomalies", "status": "success", "last_exec_time": 1.8, "median_exec_time": 1.55, "exec_time_change_rate": 16.129032258064523, "totals": {"errors": 0, "success": 2}, "runs": [{"id": "0252f707-dfc4-4aeb-b95d-450915338ae9", "time_utc": "2023-01-02T10:41:32+00:00", "status": "success", "full_refresh": false, "materialization": "view", "execution_time": 1.3}, {"id": "69e51fb5-f09e-4cc1-9286-d177457e16f8", "time_utc": "2023-01-02T10:43:46+00:00", "status": "success", "full_refresh": false, "materialization": "view", "execution_time": 1.8}]}], "model_runs_totals": {"model.elementary_integration_tests.error_model": {"errors": 3, "warnings": 0, "failures": 0, "passed": 0}, "model.elementary_integration_tests.any_type_column_anomalies": {"errors": 0, "warnings": 0, "failures": 0, "passed": 2}, "model.elementary_integration_tests.no_timestamp_anomalies": {"errors": 0, "warnings": 0, "failures": 0, "passed": 2}, "model.elementary_integration_tests.groups": {"errors": 0, "warnings": 0, "failures": 0, "passed": 2}, "model.elementary_integration_tests.nested": {"errors": 0, "warnings": 0, "failures": 0, "passed": 2}, "model.elementary_integration_tests.numeric_column_anomalies": {"errors": 0, "warnings": 0, "failures": 0, "passed": 2}, "model.elementary_integration_tests.string_column_anomalies": {"errors": 0, "warnings": 0, "failures": 0, "passed": 2}, "model.elementary_integration_tests.stats_players": {"errors": 0, "warnings": 0, "failures": 0, "passed": 2}, "model.elementary_integration_tests.stats_team": {"errors": 0, "warnings": 0, "failures": 0, "passed": 2}, "model.elementary_integration_tests.dimension_anomalies": {"errors": 0, "warnings": 0, "failures": 0, "passed": 2}}, "filters": {"test_results": [{"name": "failures", "display_name": "Failures", "model_unique_ids": ["source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "model.elementary_integration_tests.stats_team", "model.elementary_integration_tests.dimension_anomalies", "source.elementary_integration_tests.training.string_column_anomalies_training", "model.elementary_integration_tests.stats_players", "model.elementary_integration_tests.any_type_column_anomalies", "source.elementary_integration_tests.training.numeric_column_anomalies_training", "model.elementary_integration_tests.string_column_anomalies", "model.elementary_integration_tests.groups", null, "model.elementary_integration_tests.numeric_column_anomalies"]}, {"name": "warnings", "display_name": "Warnings", "model_unique_ids": ["model.elementary_integration_tests.any_type_column_anomalies"]}, {"name": "errors", "display_name": "Errors", "model_unique_ids": ["model.elementary_integration_tests.error_model"]}, {"name": "passed", "display_name": "Passed", "model_unique_ids": ["model.elementary_integration_tests.stats_team", "model.elementary_integration_tests.dimension_anomalies", "source.elementary_integration_tests.training.any_type_column_anomalies_training", "model.elementary_integration_tests.stats_players", "model.elementary_integration_tests.any_type_column_anomalies", "model.elementary_integration_tests.string_column_anomalies", "model.elementary_integration_tests.groups", "model.elementary_integration_tests.numeric_column_anomalies"]}, {"name": "no_test", "display_name": "No Tests", "model_unique_ids": ["model.elementary_integration_tests.no_timestamp_anomalies", "model.elementary_integration_tests.nested"]}], "test_runs": [{"name": "failures", "display_name": "Failures", "model_unique_ids": ["source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "model.elementary_integration_tests.stats_team", "model.elementary_integration_tests.dimension_anomalies", "source.elementary_integration_tests.training.string_column_anomalies_training", "model.elementary_integration_tests.stats_players", "model.elementary_integration_tests.any_type_column_anomalies", "source.elementary_integration_tests.training.numeric_column_anomalies_training", "model.elementary_integration_tests.string_column_anomalies", "model.elementary_integration_tests.groups", null, "model.elementary_integration_tests.numeric_column_anomalies"]}, {"name": "warnings", "display_name": "Warnings", "model_unique_ids": ["model.elementary_integration_tests.any_type_column_anomalies"]}, {"name": "errors", "display_name": "Errors", "model_unique_ids": ["model.elementary_integration_tests.error_model"]}, {"name": "passed", "display_name": "Passed", "model_unique_ids": ["model.elementary_integration_tests.stats_team", "model.elementary_integration_tests.dimension_anomalies", "source.elementary_integration_tests.training.any_type_column_anomalies_training", "model.elementary_integration_tests.stats_players", "model.elementary_integration_tests.any_type_column_anomalies", "model.elementary_integration_tests.string_column_anomalies", "model.elementary_integration_tests.groups", "model.elementary_integration_tests.numeric_column_anomalies"]}, {"name": "no_test", "display_name": "No Tests", "model_unique_ids": ["model.elementary_integration_tests.no_timestamp_anomalies", "model.elementary_integration_tests.nested"]}], "model_runs": [{"name": "success", "display_name": "Successful Runs", "model_unique_ids": ["model.elementary_integration_tests.stats_team", "model.elementary_integration_tests.dimension_anomalies", "model.elementary_integration_tests.no_timestamp_anomalies", "model.elementary_integration_tests.any_type_column_anomalies", "model.elementary_integration_tests.stats_players", "model.elementary_integration_tests.string_column_anomalies", "model.elementary_integration_tests.groups", "model.elementary_integration_tests.nested", "model.elementary_integration_tests.numeric_column_anomalies"]}, {"name": "errors", "display_name": "Failed Runs", "model_unique_ids": ["model.elementary_integration_tests.error_model"]}]}, "lineage": {"nodes": [{"id": "model.elementary_integration_tests.stats_team", "type": "model"}, {"id": "model.elementary_integration_tests.dimension_anomalies", "type": "model"}, {"id": "model.elementary_integration_tests.stats_players", "type": "model"}, {"id": "model.elementary_integration_tests.groups", "type": "model"}, {"id": "model.elementary_integration_tests.no_timestamp_anomalies", "type": "model"}, {"id": "model.elementary_integration_tests.numeric_column_anomalies", "type": "model"}, {"id": "model.elementary_integration_tests.string_column_anomalies", "type": "model"}, {"id": "model.elementary_integration_tests.any_type_column_anomalies", "type": "model"}, {"id": "model.elementary_integration_tests.error_model", "type": "model"}, {"id": "model.elementary_integration_tests.nested", "type": "model"}, {"id": "source.elementary_integration_tests.training.any_type_column_anomalies_training", "type": "source"}, {"id": "source.elementary_integration_tests.training.string_column_anomalies_training", "type": "source"}, {"id": "source.elementary_integration_tests.training.numeric_column_anomalies_training", "type": "source"}, {"id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "type": "source"}, {"id": "exposure.elementary_integration_tests.elementary_exposure", "type": "exposure"}, {"id": "exposure.elementary_integration_tests.weekly_jaffle_metrics", "type": "exposure"}], "edges": [["exposure.elementary_integration_tests.elementary_exposure", "source.elementary_integration_tests.training.any_type_column_anomalies_training"], ["exposure.elementary_integration_tests.elementary_exposure", "model.elementary_integration_tests.error_model"], ["exposure.elementary_integration_tests.weekly_jaffle_metrics", "model.elementary_integration_tests.string_column_anomalies"], ["exposure.elementary_integration_tests.weekly_jaffle_metrics", "model.elementary_integration_tests.numeric_column_anomalies"]]}, "tracking": {"posthog_api_key": "phc_56XBEzZmh02mGkadqLiYW51eECyYKWPyecVwkGdGUfg", "report_generator_anonymous_user_id": "28232c17-fa9a-4be5-af1e-acfbc519a0b0", "anonymous_warehouse_id": "d85dcf226bee445349fe2f1d0bd60c5f54f9177170087d2610569dd0f297c1a2"}, "env": {"project_name": "elementary_integration_tests", "env": "dev"}} \ No newline at end of file +{"creation_time": "2023-05-31T07:09:02+00:00", "days_back": 7, "models": {"model.elementary_integration_tests.stats_team": {"name": "stats_team", "unique_id": "model.elementary_integration_tests.stats_team", "owners": [], "tags": [], "package_name": "elementary_integration_tests", "description": "", "full_path": "models/stats_team.sql", "meta": null, "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "stats_team", "model_name": "stats_team", "normalized_full_path": "elementary_integration_tests/models/stats_team.sql", "artifact_type": "model"}, "model.elementary_integration_tests.copy_numeric_column_anomalies": {"name": "copy_numeric_column_anomalies", "unique_id": "model.elementary_integration_tests.copy_numeric_column_anomalies", "owners": [], "tags": [], "package_name": "elementary_integration_tests", "description": "", "full_path": "models/copy_numeric_column_anomalies.sql", "meta": null, "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "copy_numeric_column_anomalies", "model_name": "copy_numeric_column_anomalies", "normalized_full_path": "elementary_integration_tests/models/copy_numeric_column_anomalies.sql", "artifact_type": "model"}, "model.elementary_integration_tests.backfill_days_column_anomalies": {"name": "backfill_days_column_anomalies", "unique_id": "model.elementary_integration_tests.backfill_days_column_anomalies", "owners": [], "tags": [], "package_name": "elementary_integration_tests", "description": "", "full_path": "models/backfill_days_column_anomalies.sql", "meta": null, "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "backfill_days_column_anomalies", "model_name": "backfill_days_column_anomalies", "normalized_full_path": "elementary_integration_tests/models/backfill_days_column_anomalies.sql", "artifact_type": "model"}, "model.elementary_integration_tests.config_levels_project": {"name": "config_levels_project", "unique_id": "model.elementary_integration_tests.config_levels_project", "owners": [], "tags": [], "package_name": "elementary_integration_tests", "description": "", "full_path": "models/config_levels_project.sql", "meta": null, "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "config_levels_project", "model_name": "config_levels_project", "normalized_full_path": "elementary_integration_tests/models/config_levels_project.sql", "artifact_type": "model"}, "model.elementary_integration_tests.one": {"name": "one", "unique_id": "model.elementary_integration_tests.one", "owners": [], "tags": [], "package_name": "elementary_integration_tests", "description": "", "full_path": "models/one.sql", "meta": null, "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "one", "model_name": "one", "normalized_full_path": "elementary_integration_tests/models/one.sql", "artifact_type": "model"}, "model.elementary_integration_tests.config_levels_test_and_model": {"name": "config_levels_test_and_model", "unique_id": "model.elementary_integration_tests.config_levels_test_and_model", "owners": [], "tags": [], "package_name": "elementary_integration_tests", "description": "", "full_path": "models/config_levels_test_and_model.sql", "meta": null, "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "config_levels_test_and_model", "model_name": "config_levels_test_and_model", "normalized_full_path": "elementary_integration_tests/models/config_levels_test_and_model.sql", "artifact_type": "model"}, "model.elementary_integration_tests.users_per_day_weekly_seasonal": {"name": "users_per_day_weekly_seasonal", "unique_id": "model.elementary_integration_tests.users_per_day_weekly_seasonal", "owners": [], "tags": [], "package_name": "elementary_integration_tests", "description": "Volume anomalies on the training should fail when seasonality is Off and pass when seasonality is On. When Validation data is added, should fail when seasonality is On and pass when seasonality is Off.\n", "full_path": "models/users_per_day_weekly_seasonal.sql", "meta": null, "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "users_per_day_weekly_seasonal", "model_name": "users_per_day_weekly_seasonal", "normalized_full_path": "elementary_integration_tests/models/users_per_day_weekly_seasonal.sql", "artifact_type": "model"}, "model.elementary_integration_tests.dimension_anomalies": {"name": "dimension_anomalies", "unique_id": "model.elementary_integration_tests.dimension_anomalies", "owners": ["egk"], "tags": [], "package_name": "elementary_integration_tests", "description": "We use this model to test dimension anomalies", "full_path": "models/dimension_anomalies.sql", "meta": null, "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "dimension_anomalies", "model_name": "dimension_anomalies", "normalized_full_path": "elementary_integration_tests/models/dimension_anomalies.sql", "artifact_type": "model"}, "model.elementary_integration_tests.stats_players": {"name": "stats_players", "unique_id": "model.elementary_integration_tests.stats_players", "owners": [], "tags": [], "package_name": "elementary_integration_tests", "description": "", "full_path": "models/stats_players.sql", "meta": null, "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "stats_players", "model_name": "stats_players", "normalized_full_path": "elementary_integration_tests/models/stats_players.sql", "artifact_type": "model"}, "model.elementary_integration_tests.groups": {"name": "groups", "unique_id": "model.elementary_integration_tests.groups", "owners": [], "tags": [], "package_name": "elementary_integration_tests", "description": "", "full_path": "models/groups.sql", "meta": null, "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "groups", "model_name": "groups", "normalized_full_path": "elementary_integration_tests/models/groups.sql", "artifact_type": "model"}, "model.elementary_integration_tests.no_timestamp_anomalies": {"name": "no_timestamp_anomalies", "unique_id": "model.elementary_integration_tests.no_timestamp_anomalies", "owners": ["elon@elementary-data.com", "or@elementary-data.com"], "tags": [], "package_name": "elementary_integration_tests", "description": "We use this model to test anomalies when there is no timestamp column", "full_path": "models/no_timestamp_anomalies.sql", "meta": null, "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "no_timestamp_anomalies", "model_name": "no_timestamp_anomalies", "normalized_full_path": "elementary_integration_tests/models/no_timestamp_anomalies.sql", "artifact_type": "model"}, "model.elementary_integration_tests.ephemeral_model": {"name": "ephemeral_model", "unique_id": "model.elementary_integration_tests.ephemeral_model", "owners": [], "tags": [], "package_name": "elementary_integration_tests", "description": "", "full_path": "models/ephemeral_model.sql", "meta": null, "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "ephemeral_model", "model_name": "ephemeral_model", "normalized_full_path": "elementary_integration_tests/models/ephemeral_model.sql", "artifact_type": "model"}, "model.elementary_integration_tests.numeric_column_anomalies": {"name": "numeric_column_anomalies", "unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "owners": [], "tags": [], "package_name": "elementary_integration_tests", "description": "", "full_path": "models/numeric_column_anomalies.sql", "meta": null, "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "model_name": "numeric_column_anomalies", "normalized_full_path": "elementary_integration_tests/models/numeric_column_anomalies.sql", "artifact_type": "model"}, "model.elementary_integration_tests.string_column_anomalies": {"name": "string_column_anomalies", "unique_id": "model.elementary_integration_tests.string_column_anomalies", "owners": ["@or"], "tags": ["marketing"], "package_name": "elementary_integration_tests", "description": "", "full_path": "models/string_column_anomalies.sql", "meta": null, "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "string_column_anomalies", "model_name": "string_column_anomalies", "normalized_full_path": "elementary_integration_tests/models/string_column_anomalies.sql", "artifact_type": "model"}, "model.elementary_integration_tests.test_alerts_union": {"name": "test_alerts_union", "unique_id": "model.elementary_integration_tests.test_alerts_union", "owners": [], "tags": [], "package_name": "elementary_integration_tests", "description": "", "full_path": "models/test_alerts_union.sql", "meta": null, "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "test_alerts_union", "model_name": "test_alerts_union", "normalized_full_path": "elementary_integration_tests/models/test_alerts_union.sql", "artifact_type": "model"}, "model.elementary_integration_tests.any_type_column_anomalies": {"name": "any_type_column_anomalies", "unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "owners": ["@edr"], "tags": [], "package_name": "elementary_integration_tests", "description": "This is a very weird description with breaklines and comma, and even a string like this 'wow'. You know, these $##$34#@#!^ can also be helpful WDYT?\n", "full_path": "models/any_type_column_anomalies.sql", "meta": null, "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "any_type_column_anomalies", "model_name": "any_type_column_anomalies", "normalized_full_path": "elementary_integration_tests/models/any_type_column_anomalies.sql", "artifact_type": "model"}, "model.elementary_integration_tests.error_model": {"name": "error_model", "unique_id": "model.elementary_integration_tests.error_model", "owners": ["@elon", "egk", "elon@elementary-data.com"], "tags": ["error_model"], "package_name": "elementary_integration_tests", "description": "We use this model to create error runs and tests", "full_path": "models/error_model.sql", "meta": null, "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "error_model", "model_name": "error_model", "normalized_full_path": "elementary_integration_tests/models/error_model.sql", "artifact_type": "model"}, "model.elementary_integration_tests.nested": {"name": "nested", "unique_id": "model.elementary_integration_tests.nested", "owners": [], "tags": [], "package_name": "elementary_integration_tests", "description": "", "full_path": "models/nested/models/tree/nested.sql", "meta": null, "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "nested", "model_name": "nested", "normalized_full_path": "elementary_integration_tests/models/nested/models/tree/nested.sql", "artifact_type": "model"}, "source.elementary_integration_tests.training.any_type_column_anomalies_training": {"name": "any_type_column_anomalies_training", "unique_id": "source.elementary_integration_tests.training.any_type_column_anomalies_training", "owners": ["@edr", "egk"], "tags": [], "package_name": "elementary_integration_tests", "description": "", "full_path": "models/schema.yml", "meta": null, "source_name": "training", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_training", "model_name": "any_type_column_anomalies_training", "normalized_full_path": "elementary_integration_tests/sources/schema.sql", "artifact_type": "source"}, "source.elementary_integration_tests.training.string_column_anomalies_training": {"name": "string_column_anomalies_training", "unique_id": "source.elementary_integration_tests.training.string_column_anomalies_training", "owners": ["@edr"], "tags": [], "package_name": "elementary_integration_tests", "description": "", "full_path": "models/schema.yml", "meta": null, "source_name": "training", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "string_column_anomalies_training", "model_name": "string_column_anomalies_training", "normalized_full_path": "elementary_integration_tests/sources/schema.sql", "artifact_type": "source"}, "source.elementary_integration_tests.training.numeric_column_anomalies_training": {"name": "numeric_column_anomalies_training", "unique_id": "source.elementary_integration_tests.training.numeric_column_anomalies_training", "owners": [], "tags": [], "package_name": "elementary_integration_tests", "description": "", "full_path": "models/schema.yml", "meta": null, "source_name": "training", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "numeric_column_anomalies_training", "model_name": "numeric_column_anomalies_training", "normalized_full_path": "elementary_integration_tests/sources/schema.sql", "artifact_type": "source"}, "source.elementary_integration_tests.training.users_per_day_weekly_seasonal_training": {"name": "users_per_day_weekly_seasonal_training", "unique_id": "source.elementary_integration_tests.training.users_per_day_weekly_seasonal_training", "owners": [], "tags": [], "package_name": "elementary_integration_tests", "description": "", "full_path": "models/schema.yml", "meta": null, "source_name": "training", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "users_per_day_weekly_seasonal_training", "model_name": "users_per_day_weekly_seasonal_training", "normalized_full_path": "elementary_integration_tests/sources/schema.sql", "artifact_type": "source"}, "source.elementary_integration_tests.validation.any_type_column_anomalies_validation": {"name": "any_type_column_anomalies_validation", "unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "owners": ["hello", "world"], "tags": [], "package_name": "elementary_integration_tests", "description": "", "full_path": "models/schema.yml", "meta": null, "source_name": "validation", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "model_name": "any_type_column_anomalies_validation", "normalized_full_path": "elementary_integration_tests/sources/schema.sql", "artifact_type": "source"}, "source.elementary_integration_tests.validation.users_per_day_weekly_seasonal_validation": {"name": "users_per_day_weekly_seasonal_validation", "unique_id": "source.elementary_integration_tests.validation.users_per_day_weekly_seasonal_validation", "owners": [], "tags": [], "package_name": "elementary_integration_tests", "description": "", "full_path": "models/schema.yml", "meta": null, "source_name": "validation", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "users_per_day_weekly_seasonal_validation", "model_name": "users_per_day_weekly_seasonal_validation", "normalized_full_path": "elementary_integration_tests/sources/schema.sql", "artifact_type": "source"}, "exposure.elementary_integration_tests.elementary_exposure": {"name": "elementary_exposure", "unique_id": "exposure.elementary_integration_tests.elementary_exposure", "owners": ["Complete Nonsense"], "tags": [], "package_name": "elementary_integration_tests", "description": "Keep calm, Elementary tests exposures.\n", "full_path": "models/schema.yml", "meta": null, "label": null, "url": "https://elementary.not.really", "type": "application", "maturity": "medium", "depends_on": null, "owner": null, "model_name": "elementary_exposure", "normalized_full_path": "elementary_integration_tests/models/schema.yml", "artifact_type": "exposure"}, "exposure.elementary_integration_tests.weekly_jaffle_metrics": {"name": "weekly_jaffle_metrics", "unique_id": "exposure.elementary_integration_tests.weekly_jaffle_metrics", "owners": ["Claire from Data"], "tags": [], "package_name": "elementary_integration_tests", "description": "Did someone say \"exponential growth\"?\n", "full_path": "models/schema.yml", "meta": null, "label": null, "url": "https://bi.tool/dashboards/1", "type": "dashboard", "maturity": "high", "depends_on": null, "owner": null, "model_name": "weekly_jaffle_metrics", "normalized_full_path": "elementary_integration_tests/models/schema.yml", "artifact_type": "exposure"}}, "sidebars": {"dbt": {"elementary_integration_tests": {"models": {"__files__": ["model.elementary_integration_tests.stats_team", "model.elementary_integration_tests.copy_numeric_column_anomalies", "model.elementary_integration_tests.backfill_days_column_anomalies", "model.elementary_integration_tests.config_levels_project", "model.elementary_integration_tests.one", "model.elementary_integration_tests.config_levels_test_and_model", "model.elementary_integration_tests.users_per_day_weekly_seasonal", "model.elementary_integration_tests.dimension_anomalies", "model.elementary_integration_tests.stats_players", "model.elementary_integration_tests.groups", "model.elementary_integration_tests.no_timestamp_anomalies", "model.elementary_integration_tests.ephemeral_model", "model.elementary_integration_tests.numeric_column_anomalies", "model.elementary_integration_tests.string_column_anomalies", "model.elementary_integration_tests.test_alerts_union", "model.elementary_integration_tests.any_type_column_anomalies", "model.elementary_integration_tests.error_model"], "nested": {"models": {"tree": {"__files__": ["model.elementary_integration_tests.nested"]}}}}, "sources": {"__files__": ["source.elementary_integration_tests.training.any_type_column_anomalies_training", "source.elementary_integration_tests.training.string_column_anomalies_training", "source.elementary_integration_tests.training.numeric_column_anomalies_training", "source.elementary_integration_tests.training.users_per_day_weekly_seasonal_training", "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "source.elementary_integration_tests.validation.users_per_day_weekly_seasonal_validation"]}}}, "tags": {"No tags": ["model.elementary_integration_tests.stats_team", "model.elementary_integration_tests.copy_numeric_column_anomalies", "model.elementary_integration_tests.backfill_days_column_anomalies", "model.elementary_integration_tests.config_levels_project", "model.elementary_integration_tests.one", "model.elementary_integration_tests.config_levels_test_and_model", "model.elementary_integration_tests.users_per_day_weekly_seasonal", "model.elementary_integration_tests.dimension_anomalies", "model.elementary_integration_tests.stats_players", "model.elementary_integration_tests.groups", "model.elementary_integration_tests.no_timestamp_anomalies", "model.elementary_integration_tests.ephemeral_model", "model.elementary_integration_tests.numeric_column_anomalies", "model.elementary_integration_tests.test_alerts_union", "model.elementary_integration_tests.any_type_column_anomalies", "model.elementary_integration_tests.nested", "source.elementary_integration_tests.training.any_type_column_anomalies_training", "source.elementary_integration_tests.training.string_column_anomalies_training", "source.elementary_integration_tests.training.numeric_column_anomalies_training", "source.elementary_integration_tests.training.users_per_day_weekly_seasonal_training", "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "source.elementary_integration_tests.validation.users_per_day_weekly_seasonal_validation"], "marketing": ["model.elementary_integration_tests.string_column_anomalies"], "error_model": ["model.elementary_integration_tests.error_model"]}, "owners": {"No owners": ["model.elementary_integration_tests.stats_team", "model.elementary_integration_tests.copy_numeric_column_anomalies", "model.elementary_integration_tests.backfill_days_column_anomalies", "model.elementary_integration_tests.config_levels_project", "model.elementary_integration_tests.one", "model.elementary_integration_tests.config_levels_test_and_model", "model.elementary_integration_tests.users_per_day_weekly_seasonal", "model.elementary_integration_tests.stats_players", "model.elementary_integration_tests.groups", "model.elementary_integration_tests.ephemeral_model", "model.elementary_integration_tests.numeric_column_anomalies", "model.elementary_integration_tests.test_alerts_union", "model.elementary_integration_tests.nested", "source.elementary_integration_tests.training.numeric_column_anomalies_training", "source.elementary_integration_tests.training.users_per_day_weekly_seasonal_training", "source.elementary_integration_tests.validation.users_per_day_weekly_seasonal_validation"], "egk": ["model.elementary_integration_tests.dimension_anomalies", "model.elementary_integration_tests.error_model", "source.elementary_integration_tests.training.any_type_column_anomalies_training"], "elon@elementary-data.com": ["model.elementary_integration_tests.no_timestamp_anomalies", "model.elementary_integration_tests.error_model"], "or@elementary-data.com": ["model.elementary_integration_tests.no_timestamp_anomalies"], "@or": ["model.elementary_integration_tests.string_column_anomalies"], "@edr": ["model.elementary_integration_tests.any_type_column_anomalies", "source.elementary_integration_tests.training.any_type_column_anomalies_training", "source.elementary_integration_tests.training.string_column_anomalies_training"], "@elon": ["model.elementary_integration_tests.error_model"], "hello": ["source.elementary_integration_tests.validation.any_type_column_anomalies_validation"], "world": ["source.elementary_integration_tests.validation.any_type_column_anomalies_validation"]}}, "invocation": {"invocation_id": null, "detected_at": null, "command": null, "selected": null, "full_refresh": null, "job_url": null, "job_name": null, "job_id": null, "orchestrator": null}, "test_results": {"model.elementary_integration_tests.one": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.accepted_values_one_one__2__3.5c148cffcc", "elementary_unique_id": "test.elementary_integration_tests.accepted_values_one_one__2__3.5c148cffcc.one.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "one", "column_name": "one", "test_name": "accepted_values", "test_display_name": "Accepted Values", "latest_run_time": "2023-05-30T20:02:44+03:00", "latest_run_time_utc": "2023-05-30T17:02:44+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.one", "table_unique_id": "elementary_tests.noya_tests.one", "test_type": "dbt_test", "test_sub_type": "generic", "test_query": "with all_values as (\n\n select\n one as value_field,\n count(*) as n_records\n\n from ELEMENTARY_TESTS.noya_tests.one\n group by one\n\n)\n\nselect *\nfrom all_values\nwhere value_field not in (\n '2','3'\n)", "test_params": {"values": [2, 3], "column_name": "one", "model": "{{ get_where_subquery(ref('one')) }}"}, "test_created_at": null, "description": "This test validates that all of the values in a column are present in a supplied list of `values`. If any values other than those provided in the list are present, then the test will fail.", "result": {"result_description": "Got 1 result, configured to fail if != 0", "result_query": "with all_values as (\n\n select\n one as value_field,\n count(*) as n_records\n\n from ELEMENTARY_TESTS.noya_tests.one\n group by one\n\n)\n\nselect *\nfrom all_values\nwhere value_field not in (\n '2','3'\n)"}, "configuration": {"test_name": "accepted_values", "test_params": {"values": [2, 3], "column_name": "one", "model": "{{ get_where_subquery(ref('one')) }}"}}}, "test_results": {"display_name": "accepted_values", "results_sample": [{"value_field": 1.0, "n_records": 1.0}], "error_message": "Got 1 result, configured to fail if != 0", "failed_rows_count": 1}}], "model.elementary_integration_tests.config_levels_project": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.config_levels_config_levels_project__period_day_count_1_.e491a0d999", "elementary_unique_id": "test.elementary_integration_tests.config_levels_config_levels_project__period_day_count_1_.e491a0d999.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "config_levels_project", "column_name": null, "test_name": "config_levels", "test_display_name": "Config Levels", "latest_run_time": "2023-05-29T18:01:12+03:00", "latest_run_time_utc": "2023-05-29T15:01:12+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.config_levels_project", "table_unique_id": "elementary_tests.noya_tests.config_levels_project", "test_type": "dbt_test", "test_sub_type": "generic", "test_query": "with nothing as (select 1 as num)\n select * from nothing where num = 2", "test_params": {"expected_config": {"time_bucket": {"period": "day", "count": 1}}, "model": "{{ get_where_subquery(ref('config_levels_project')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": "with nothing as (select 1 as num)\n select * from nothing where num = 2"}, "configuration": {"test_name": "config_levels", "test_params": {"expected_config": {"time_bucket": {"period": "day", "count": 1}}, "model": "{{ get_where_subquery(ref('config_levels_project')) }}"}}}, "test_results": {"display_name": "config_levels", "results_sample": null, "error_message": null, "failed_rows_count": -1}}], "model.elementary_integration_tests.config_levels_test_and_model": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.config_levels_config_levels_test_and_model__period_day_count_3_.462f85ebd5", "elementary_unique_id": "test.elementary_integration_tests.config_levels_config_levels_test_and_model__period_day_count_3_.462f85ebd5.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "config_levels_test_and_model", "column_name": null, "test_name": "config_levels", "test_display_name": "Config Levels", "latest_run_time": "2023-05-29T18:01:12+03:00", "latest_run_time_utc": "2023-05-29T15:01:12+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.config_levels_test_and_model", "table_unique_id": "elementary_tests.noya_tests.config_levels_test_and_model", "test_type": "dbt_test", "test_sub_type": "generic", "test_query": "with nothing as (select 1 as num)\n select * from nothing where num = 2", "test_params": {"expected_config": {"time_bucket": {"period": "day", "count": 3}}, "model": "{{ get_where_subquery(ref('config_levels_test_and_model')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": "with nothing as (select 1 as num)\n select * from nothing where num = 2"}, "configuration": {"test_name": "config_levels", "test_params": {"expected_config": {"time_bucket": {"period": "day", "count": 3}}, "model": "{{ get_where_subquery(ref('config_levels_test_and_model')) }}"}}}, "test_results": {"display_name": "config_levels", "results_sample": null, "error_message": null, "failed_rows_count": -1}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.config_levels_config_levels_test_and_model__period_hour_count_4___hour__4.4745d4716f", "elementary_unique_id": "test.elementary_integration_tests.config_levels_config_levels_test_and_model__period_hour_count_4___hour__4.4745d4716f.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "config_levels_test_and_model", "column_name": null, "test_name": "config_levels", "test_display_name": "Config Levels", "latest_run_time": "2023-05-29T18:01:12+03:00", "latest_run_time_utc": "2023-05-29T15:01:12+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.config_levels_test_and_model", "table_unique_id": "elementary_tests.noya_tests.config_levels_test_and_model", "test_type": "dbt_test", "test_sub_type": "generic", "test_query": "with nothing as (select 1 as num)\n select * from nothing where num = 2", "test_params": {"time_bucket": {"period": "hour", "count": 4}, "expected_config": {"time_bucket": {"period": "hour", "count": 4}}, "model": "{{ get_where_subquery(ref('config_levels_test_and_model')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": "with nothing as (select 1 as num)\n select * from nothing where num = 2"}, "configuration": {"test_name": "config_levels", "test_params": {"time_bucket": {"period": "hour", "count": 4}, "expected_config": {"time_bucket": {"period": "hour", "count": 4}}, "model": "{{ get_where_subquery(ref('config_levels_test_and_model')) }}"}}}, "test_results": {"display_name": "config_levels", "results_sample": null, "error_message": null, "failed_rows_count": -1}}], "model.elementary_integration_tests.any_type_column_anomalies": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "elementary_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "any_type_column_anomalies", "column_name": null, "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"exclude_regexp": ".*column1|column2|column3|column4|column5|column6|column7|column8|column9|column10|column11|column12|column13|column14|column15|column16|column17.*", "model": "{{ get_where_subquery(ref('any_type_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies_drop.3e202bb243", "elementary_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies_drop.3e202bb243.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "any_type_column_anomalies", "column_name": null, "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"anomaly_direction": "drop", "model": "{{ get_where_subquery(ref('any_type_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies_spike.1f1909a57c", "elementary_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies_spike.1f1909a57c.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "any_type_column_anomalies", "column_name": null, "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"anomaly_direction": "spike", "model": "{{ get_where_subquery(ref('any_type_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "elementary_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "any_type_column_anomalies", "column_name": null, "test_name": "volume_anomalies", "test_display_name": "Volume Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"time_bucket": {"period": "hour", "count": 4}, "model": "{{ get_where_subquery(ref('any_type_column_anomalies')) }}"}, "test_created_at": null, "description": "This is a very weird description with breaklines and comma, and even a string like this 'wow'. You know, these $##$34#@#!^ can also be helpful WDYT?\n", "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "volume_anomalies", "timestamp_column": null, "testing_timeframe": "4 hours", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_week__1.ef6d0eff06", "elementary_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_week__1.ef6d0eff06.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "any_type_column_anomalies", "column_name": null, "test_name": "volume_anomalies", "test_display_name": "Volume Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"time_bucket": {"period": "week", "count": 1}, "model": "{{ get_where_subquery(ref('any_type_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "volume_anomalies", "timestamp_column": null, "testing_timeframe": "1 week", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.generic_test_on_model_any_type_column_anomalies_.a9e77d8087", "elementary_unique_id": "test.elementary_integration_tests.generic_test_on_model_any_type_column_anomalies_.a9e77d8087.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "any_type_column_anomalies", "column_name": null, "test_name": "generic_test_on_model", "test_display_name": "Generic Test On Model", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.any_type_column_anomalies", "test_type": "dbt_test", "test_sub_type": "generic", "test_query": null, "test_params": {"model": "{{ get_where_subquery(ref('any_type_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "generic_test_on_model", "test_params": {"model": "{{ get_where_subquery(ref('any_type_column_anomalies')) }}"}}}, "test_results": {"display_name": "generic_test_on_model", "results_sample": null, "error_message": null, "failed_rows_count": -1}}], "model.elementary_integration_tests.copy_numeric_column_anomalies": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148", "elementary_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "copy_numeric_column_anomalies", "column_name": null, "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.copy_numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.copy_numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_anomalies": ["zero_count"], "model": "{{ get_where_subquery(ref('copy_numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}], "model.elementary_integration_tests.ephemeral_model": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_ephemeral_model_.69d9c5e486", "elementary_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_ephemeral_model_.69d9c5e486.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "ephemeral_model", "column_name": null, "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.ephemeral_model", "table_unique_id": "elementary_tests.noya_tests.ephemeral_model", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"model": "{{ get_where_subquery(ref('ephemeral_model')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_ephemeral_model_.085bb21469", "elementary_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_ephemeral_model_.085bb21469.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "ephemeral_model", "column_name": null, "test_name": "freshness_anomalies", "test_display_name": "Freshness Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.ephemeral_model", "table_unique_id": "elementary_tests.noya_tests.ephemeral_model", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"model": "{{ get_where_subquery(ref('ephemeral_model')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "freshness_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_schema_changes_ephemeral_model_.4470433d4c", "elementary_unique_id": "test.elementary_integration_tests.elementary_schema_changes_ephemeral_model_.4470433d4c", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "ephemeral_model", "column_name": null, "test_name": "schema_changes", "test_display_name": "Schema Changes", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.ephemeral_model", "table_unique_id": "elementary_tests.noya_tests.ephemeral_model", "test_type": "schema_change", "test_sub_type": "generic", "test_query": null, "test_params": {"model": "{{ get_where_subquery(ref('ephemeral_model')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "schema_changes", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_ephemeral_model_.4b08aa00b7", "elementary_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_ephemeral_model_.4b08aa00b7.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "ephemeral_model", "column_name": null, "test_name": "volume_anomalies", "test_display_name": "Volume Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.ephemeral_model", "table_unique_id": "elementary_tests.noya_tests.ephemeral_model", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"model": "{{ get_where_subquery(ref('ephemeral_model')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "volume_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}], "model.elementary_integration_tests.numeric_column_anomalies": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_numeric_column_anomalies_average_length__null_count.4719a95b87", "elementary_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_numeric_column_anomalies_average_length__null_count.4719a95b87.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": null, "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_anomalies": ["average_length", "null_count"], "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde.average.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": "average", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_anomalies": ["average"], "column_name": "average", "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb.max.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": "max", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_anomalies": ["average"], "column_name": "max", "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7.min.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": "min", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_anomalies": ["average"], "column_name": "min", "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_drop__average__max.e87fc4578f", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_drop__average__max.e87fc4578f.max.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": "max", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_anomalies": ["average"], "anomaly_direction": "drop", "column_name": "max", "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba.average.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": "average", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_anomalies": ["max"], "column_name": "average", "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__max.21a73d9fec", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__max.21a73d9fec.max.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": "max", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_anomalies": ["max"], "column_name": "max", "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__min.6758a0b107", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__min.6758a0b107.min.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": "min", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_anomalies": ["max"], "column_name": "min", "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e.average.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": "average", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_anomalies": ["min"], "column_name": "average", "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__max.9841e551cc", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__max.9841e551cc.max.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": "max", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_anomalies": ["min"], "column_name": "max", "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__min.72357fb8ab", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__min.72357fb8ab.min.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": "min", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_anomalies": ["min"], "column_name": "min", "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_spike__average__max.d74f23e2ef", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_spike__average__max.d74f23e2ef.max.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": "max", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_anomalies": ["average"], "anomaly_direction": "spike", "column_name": "max", "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84.standard_deviation.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": "standard_deviation", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_name": "standard_deviation", "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_sum__sum.6ede4629eb", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_sum__sum.6ede4629eb.sum.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": "sum", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_anomalies": ["sum"], "column_name": "sum", "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_updated_at.b0d22411ff", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_updated_at.b0d22411ff.updated_at.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": "updated_at", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_name": "updated_at", "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_variance.ccbeab9e37", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_variance.ccbeab9e37.variance.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": "variance", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_name": "variance", "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_count.35e5387f41", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_count.35e5387f41.zero_count.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": "zero_count", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_name": "zero_count", "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8.zero_percent.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": "zero_percent", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_name": "zero_percent", "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_event_freshness_anomalies_numeric_column_anomalies_occurred_at__updated_at.1ca7b9299b", "elementary_unique_id": "test.elementary_integration_tests.elementary_event_freshness_anomalies_numeric_column_anomalies_occurred_at__updated_at.1ca7b9299b.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": null, "test_name": "event_freshness_anomalies", "test_display_name": "Event Freshness Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"event_timestamp_column": "occurred_at", "update_timestamp_column": "updated_at", "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "event_freshness_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "elementary_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": null, "test_name": "freshness_anomalies", "test_display_name": "Freshness Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "freshness_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_schema_changes_numeric_column_anomalies_.1d9f82cc93", "elementary_unique_id": "test.elementary_integration_tests.elementary_schema_changes_numeric_column_anomalies_.1d9f82cc93", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": null, "test_name": "schema_changes", "test_display_name": "Schema Changes", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "schema_change", "test_sub_type": "generic", "test_query": null, "test_params": {"model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "schema_changes", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "elementary_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": null, "test_name": "volume_anomalies", "test_display_name": "Volume Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "volume_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_drop.c2dea8af95", "elementary_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_drop.c2dea8af95.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": null, "test_name": "volume_anomalies", "test_display_name": "Volume Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"anomaly_direction": "drop", "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "volume_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_spike.d99efa2f8a", "elementary_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_spike.d99efa2f8a.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": null, "test_name": "volume_anomalies", "test_display_name": "Volume Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"anomaly_direction": "spike", "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "volume_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.singular_test_with_one_ref", "elementary_unique_id": "test.elementary_integration_tests.singular_test_with_one_ref.None.singular", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": null, "test_name": "singular_test_with_one_ref", "test_display_name": "Singular Test With One Ref", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "dbt_test", "test_sub_type": "singular", "test_query": null, "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "singular_test_with_one_ref", "test_params": {}}}, "test_results": {"display_name": "singular_test_with_one_ref", "results_sample": null, "error_message": null, "failed_rows_count": -1}}], "model.elementary_integration_tests.string_column_anomalies": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_string_column_anomalies_.151e5a09d5", "elementary_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_string_column_anomalies_.151e5a09d5.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "string_column_anomalies", "column_name": null, "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"model": "{{ get_where_subquery(ref('string_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2.average_length.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "string_column_anomalies", "column_name": "average_length", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_anomalies": ["average_length", "null_count"], "column_name": "average_length", "model": "{{ get_where_subquery(ref('string_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_max_length.5c7beb9c06", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_max_length.5c7beb9c06.max_length.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "string_column_anomalies", "column_name": "max_length", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_name": "max_length", "model": "{{ get_where_subquery(ref('string_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8.min_length.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "string_column_anomalies", "column_name": "min_length", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_anomalies": ["min_length", "max_length", "missing_count"], "column_name": "min_length", "model": "{{ get_where_subquery(ref('string_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d.missing_count.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "string_column_anomalies", "column_name": "missing_count", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_name": "missing_count", "model": "{{ get_where_subquery(ref('string_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771.missing_percent.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "string_column_anomalies", "column_name": "missing_percent", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_name": "missing_percent", "model": "{{ get_where_subquery(ref('string_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_updated_at.8901e974a6", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_updated_at.8901e974a6.updated_at.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "string_column_anomalies", "column_name": "updated_at", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_name": "updated_at", "model": "{{ get_where_subquery(ref('string_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_event_freshness_anomalies_string_column_anomalies_occurred_at__updated_at.e4db4306c6", "elementary_unique_id": "test.elementary_integration_tests.elementary_event_freshness_anomalies_string_column_anomalies_occurred_at__updated_at.e4db4306c6.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "string_column_anomalies", "column_name": null, "test_name": "event_freshness_anomalies", "test_display_name": "Event Freshness Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"event_timestamp_column": "occurred_at", "update_timestamp_column": "updated_at", "model": "{{ get_where_subquery(ref('string_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "event_freshness_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "elementary_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "string_column_anomalies", "column_name": null, "test_name": "freshness_anomalies", "test_display_name": "Freshness Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"model": "{{ get_where_subquery(ref('string_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "freshness_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_schema_changes_string_column_anomalies_.c57cd75b87", "elementary_unique_id": "test.elementary_integration_tests.elementary_schema_changes_string_column_anomalies_.c57cd75b87", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "string_column_anomalies", "column_name": null, "test_name": "schema_changes", "test_display_name": "Schema Changes", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.string_column_anomalies", "test_type": "schema_change", "test_sub_type": "generic", "test_query": null, "test_params": {"model": "{{ get_where_subquery(ref('string_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "schema_changes", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.relationships_string_column_anomalies_min_length__max_length__source_training_string_column_anomalies_training_.a2cbc353c6", "elementary_unique_id": "test.elementary_integration_tests.relationships_string_column_anomalies_min_length__max_length__source_training_string_column_anomalies_training_.a2cbc353c6.min_length.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "string_column_anomalies", "column_name": "min_length", "test_name": "relationships", "test_display_name": "Relationships", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.string_column_anomalies", "test_type": "dbt_test", "test_sub_type": "generic", "test_query": null, "test_params": {"to": "source('training', 'string_column_anomalies_training')", "field": "max_length", "column_name": "min_length", "model": "{{ get_where_subquery(ref('string_column_anomalies')) }}"}, "test_created_at": null, "description": "This test validates that all of the records in a child table have a corresponding record in a parent table. This property is referred to as \"referential integrity\".", "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "relationships", "test_params": {"to": "source('training', 'string_column_anomalies_training')", "field": "max_length", "column_name": "min_length", "model": "{{ get_where_subquery(ref('string_column_anomalies')) }}"}}}, "test_results": {"display_name": "relationships", "results_sample": null, "error_message": null, "failed_rows_count": -1}}], "model.elementary_integration_tests.backfill_days_column_anomalies": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_backfill_days_column_anomalies_7__min_length__max_length__min_length.437faf5372", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_backfill_days_column_anomalies_7__min_length__max_length__min_length.437faf5372.min_length.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "backfill_days_column_anomalies", "column_name": "min_length", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.backfill_days_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.backfill_days_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"backfill_days": 7, "column_anomalies": ["min_length", "max_length"], "column_name": "min_length", "model": "{{ get_where_subquery(ref('backfill_days_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_backfill_days_column_anomalies_min_length__max_length__min_length.9cf2f5f6ad", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_backfill_days_column_anomalies_min_length__max_length__min_length.9cf2f5f6ad.min_length.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "backfill_days_column_anomalies", "column_name": "min_length", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.backfill_days_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.backfill_days_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_anomalies": ["min_length", "max_length"], "column_name": "min_length", "model": "{{ get_where_subquery(ref('backfill_days_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}], "model.elementary_integration_tests.no_timestamp_anomalies": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_no_timestamp_anomalies_null_count__null_count_str.6532606df5", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_no_timestamp_anomalies_null_count__null_count_str.6532606df5.null_count_str.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "no_timestamp_anomalies", "column_name": "null_count_str", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.no_timestamp_anomalies", "table_unique_id": "elementary_tests.noya_tests.no_timestamp_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_anomalies": ["null_count"], "column_name": "null_count_str", "model": "{{ get_where_subquery(ref('no_timestamp_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_dimension_anomalies_no_timestamp_anomalies_null_count_str.cf20940f2d", "elementary_unique_id": "test.elementary_integration_tests.elementary_dimension_anomalies_no_timestamp_anomalies_null_count_str.cf20940f2d", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "no_timestamp_anomalies", "column_name": null, "test_name": "dimension_anomalies", "test_display_name": "Dimension Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.no_timestamp_anomalies", "table_unique_id": "elementary_tests.noya_tests.no_timestamp_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"dimensions": ["null_count_str"], "model": "{{ get_where_subquery(ref('no_timestamp_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "dimension_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_no_timestamp_anomalies_.73ede8a6cc", "elementary_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_no_timestamp_anomalies_.73ede8a6cc.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "no_timestamp_anomalies", "column_name": null, "test_name": "volume_anomalies", "test_display_name": "Volume Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.no_timestamp_anomalies", "table_unique_id": "elementary_tests.noya_tests.no_timestamp_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"model": "{{ get_where_subquery(ref('no_timestamp_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "volume_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}], "model.elementary_integration_tests.dimension_anomalies": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_dimension_anomalies_dimension_anomalies_drop__platform.021a36a88a", "elementary_unique_id": "test.elementary_integration_tests.elementary_dimension_anomalies_dimension_anomalies_drop__platform.021a36a88a", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "dimension_anomalies", "column_name": null, "test_name": "dimension_anomalies", "test_display_name": "Dimension Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.dimension_anomalies", "table_unique_id": "elementary_tests.noya_tests.dimension_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"anomaly_direction": "drop", "dimensions": ["platform"], "model": "{{ get_where_subquery(ref('dimension_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "dimension_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_dimension_anomalies_dimension_anomalies_platform.cf343e4b29", "elementary_unique_id": "test.elementary_integration_tests.elementary_dimension_anomalies_dimension_anomalies_platform.cf343e4b29", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "dimension_anomalies", "column_name": null, "test_name": "dimension_anomalies", "test_display_name": "Dimension Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.dimension_anomalies", "table_unique_id": "elementary_tests.noya_tests.dimension_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"dimensions": ["platform"], "model": "{{ get_where_subquery(ref('dimension_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "dimension_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_dimension_anomalies_dimension_anomalies_platform__platform_android_.89f503656a", "elementary_unique_id": "test.elementary_integration_tests.elementary_dimension_anomalies_dimension_anomalies_platform__platform_android_.89f503656a", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "dimension_anomalies", "column_name": null, "test_name": "dimension_anomalies", "test_display_name": "Dimension Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.dimension_anomalies", "table_unique_id": "elementary_tests.noya_tests.dimension_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"dimensions": ["platform"], "where_expression": "platform = 'android'", "model": "{{ get_where_subquery(ref('dimension_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "dimension_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_dimension_anomalies_dimension_anomalies_platform__version.b65d46fbf9", "elementary_unique_id": "test.elementary_integration_tests.elementary_dimension_anomalies_dimension_anomalies_platform__version.b65d46fbf9", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "dimension_anomalies", "column_name": null, "test_name": "dimension_anomalies", "test_display_name": "Dimension Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.dimension_anomalies", "table_unique_id": "elementary_tests.noya_tests.dimension_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"dimensions": ["platform", "version"], "model": "{{ get_where_subquery(ref('dimension_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "dimension_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_dimension_anomalies_dimension_anomalies_spike__platform.ae9aad0d02", "elementary_unique_id": "test.elementary_integration_tests.elementary_dimension_anomalies_dimension_anomalies_spike__platform.ae9aad0d02", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "dimension_anomalies", "column_name": null, "test_name": "dimension_anomalies", "test_display_name": "Dimension Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.dimension_anomalies", "table_unique_id": "elementary_tests.noya_tests.dimension_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"anomaly_direction": "spike", "dimensions": ["platform"], "model": "{{ get_where_subquery(ref('dimension_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "dimension_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}], "model.elementary_integration_tests.groups": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_schema_changes_from_baseline_groups_True.7468e2e161", "elementary_unique_id": "test.elementary_integration_tests.elementary_schema_changes_from_baseline_groups_True.7468e2e161", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "groups", "column_name": null, "test_name": "schema_changes_from_baseline", "test_display_name": "Schema Changes From Baseline", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.groups", "table_unique_id": "elementary_tests.noya_tests.groups", "test_type": "schema_change", "test_sub_type": "generic", "test_query": null, "test_params": {"enforce_types": true, "model": "{{ get_where_subquery(ref('groups')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "schema_changes_from_baseline", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_schema_changes_from_baseline_groups_True.973df95f83", "elementary_unique_id": "test.elementary_integration_tests.elementary_schema_changes_from_baseline_groups_True.973df95f83", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "groups", "column_name": null, "test_name": "schema_changes_from_baseline", "test_display_name": "Schema Changes From Baseline", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.groups", "table_unique_id": "elementary_tests.noya_tests.groups", "test_type": "schema_change", "test_sub_type": "generic", "test_query": null, "test_params": {"fail_on_added": true, "model": "{{ get_where_subquery(ref('groups')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "schema_changes_from_baseline", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_schema_changes_groups_.b321d3c7ba", "elementary_unique_id": "test.elementary_integration_tests.elementary_schema_changes_groups_.b321d3c7ba", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "groups", "column_name": null, "test_name": "schema_changes", "test_display_name": "Schema Changes", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.groups", "table_unique_id": "elementary_tests.noya_tests.groups", "test_type": "schema_change", "test_sub_type": "generic", "test_query": null, "test_params": {"model": "{{ get_where_subquery(ref('groups')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "schema_changes", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "generic", "metrics": null, "result_description": null}}], "model.elementary_integration_tests.stats_players": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_schema_changes_from_baseline_stats_players_.02157887f9", "elementary_unique_id": "test.elementary_integration_tests.elementary_schema_changes_from_baseline_stats_players_.02157887f9", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "stats_players", "column_name": null, "test_name": "schema_changes_from_baseline", "test_display_name": "Schema Changes From Baseline", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.stats_players", "table_unique_id": "elementary_tests.noya_tests.stats_players", "test_type": "schema_change", "test_sub_type": "generic", "test_query": null, "test_params": {"model": "{{ get_where_subquery(ref('stats_players')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "schema_changes_from_baseline", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_schema_changes_from_baseline_stats_players_True.1447622942", "elementary_unique_id": "test.elementary_integration_tests.elementary_schema_changes_from_baseline_stats_players_True.1447622942", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "stats_players", "column_name": null, "test_name": "schema_changes_from_baseline", "test_display_name": "Schema Changes From Baseline", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.stats_players", "table_unique_id": "elementary_tests.noya_tests.stats_players", "test_type": "schema_change", "test_sub_type": "generic", "test_query": null, "test_params": {"enforce_types": true, "model": "{{ get_where_subquery(ref('stats_players')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "schema_changes_from_baseline", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_schema_changes_stats_players_.388c33d77b", "elementary_unique_id": "test.elementary_integration_tests.elementary_schema_changes_stats_players_.388c33d77b", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "stats_players", "column_name": null, "test_name": "schema_changes", "test_display_name": "Schema Changes", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.stats_players", "table_unique_id": "elementary_tests.noya_tests.stats_players", "test_type": "schema_change", "test_sub_type": "generic", "test_query": null, "test_params": {"model": "{{ get_where_subquery(ref('stats_players')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "schema_changes", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "generic", "metrics": null, "result_description": null}}], "model.elementary_integration_tests.stats_team": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_schema_changes_stats_team_.fe2147cc27", "elementary_unique_id": "test.elementary_integration_tests.elementary_schema_changes_stats_team_.fe2147cc27", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "stats_team", "column_name": null, "test_name": "schema_changes", "test_display_name": "Schema Changes", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.stats_team", "table_unique_id": "elementary_tests.noya_tests.stats_team", "test_type": "schema_change", "test_sub_type": "generic", "test_query": null, "test_params": {"model": "{{ get_where_subquery(ref('stats_team')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "schema_changes", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "generic", "metrics": null, "result_description": null}}], "source.elementary_integration_tests.validation.any_type_column_anomalies_validation": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_BOOL.null_count", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_BOOL", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "null_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_BOOL' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_BOOL' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Null Count", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_BOOL.null_percent", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_BOOL", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "null_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_BOOL' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_BOOL' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Null Percent", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_FLOAT.average", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "average", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Average", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_FLOAT.max", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "max", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('max' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('max' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Max", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_FLOAT.min", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "min", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('min' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('min' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Min", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_FLOAT.null_count", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "null_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Null Count", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_FLOAT.null_percent", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "null_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Null Percent", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_FLOAT.standard_deviation", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "standard_deviation", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('standard_deviation' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('standard_deviation' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Standard Deviation", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_FLOAT.variance", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "variance", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('variance' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('variance' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Variance", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_FLOAT.zero_count", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "zero_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('zero_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('zero_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Zero Count", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_FLOAT.zero_percent", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "zero_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('zero_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('zero_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Zero Percent", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_INT.average", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "average", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Average", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_INT.max", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "max", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('max' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('max' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Max", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_INT.min", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "min", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('min' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('min' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Min", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_INT.null_count", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "null_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Null Count", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_INT.null_percent", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "null_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Null Percent", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_INT.standard_deviation", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "standard_deviation", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('standard_deviation' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('standard_deviation' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Standard Deviation", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_INT.variance", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "variance", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('variance' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('variance' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Variance", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_INT.zero_count", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "zero_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('zero_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('zero_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Zero Count", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_INT.zero_percent", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "zero_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('zero_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('zero_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Zero Percent", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_STR.average_length", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "average_length", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('average_length' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('average_length' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Average Length", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_STR.max_length", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "max_length", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('max_length' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('max_length' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Max Length", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_STR.min_length", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "min_length", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('min_length' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('min_length' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Min Length", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_STR.missing_count", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "missing_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('missing_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('missing_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Missing Count", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_STR.missing_percent", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "missing_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('missing_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('missing_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Missing Percent", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_STR.null_count", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "null_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Null Count", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_STR.null_percent", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "null_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Null Percent", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_BOOL.null_count", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_BOOL", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "null_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_BOOL' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_BOOL' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Null Count", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_BOOL.null_percent", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_BOOL", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "null_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_BOOL' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_BOOL' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Null Percent", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_FLOAT.average", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "average", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Average", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_FLOAT.max", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "max", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('max' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('max' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Max", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_FLOAT.min", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "min", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('min' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('min' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Min", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_FLOAT.null_count", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "null_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Null Count", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_FLOAT.null_percent", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "null_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Null Percent", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_FLOAT.standard_deviation", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "standard_deviation", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('standard_deviation' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('standard_deviation' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Standard Deviation", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_FLOAT.variance", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "variance", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('variance' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('variance' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Variance", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_FLOAT.zero_count", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "zero_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('zero_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('zero_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Zero Count", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_FLOAT.zero_percent", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "zero_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('zero_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('zero_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Zero Percent", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_INT.average", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "average", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Average", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_INT.max", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "max", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('max' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('max' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Max", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_INT.min", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "min", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('min' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('min' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Min", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_INT.null_count", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "null_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Null Count", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_INT.null_percent", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "null_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Null Percent", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_INT.standard_deviation", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "standard_deviation", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('standard_deviation' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('standard_deviation' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Standard Deviation", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_INT.variance", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "variance", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('variance' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('variance' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Variance", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_INT.zero_count", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "zero_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('zero_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('zero_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Zero Count", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_INT.zero_percent", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "zero_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('zero_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('zero_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Zero Percent", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_STR.average_length", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "average_length", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('average_length' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('average_length' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Average Length", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_STR.max_length", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "max_length", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('max_length' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('max_length' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Max Length", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_STR.min_length", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "min_length", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('min_length' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('min_length' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Min Length", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_STR.missing_count", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "missing_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('missing_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('missing_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Missing Count", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_STR.missing_percent", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "missing_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('missing_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('missing_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Missing Percent", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_STR.null_count", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "null_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Null Count", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_STR.null_percent", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "null_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Null Percent", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.OCCURRED_AT.null_count", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "OCCURRED_AT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "null_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('OCCURRED_AT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('OCCURRED_AT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Null Count", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.OCCURRED_AT.null_percent", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "OCCURRED_AT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "null_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('OCCURRED_AT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('OCCURRED_AT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Null Percent", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.UPDATED_AT.null_count", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "UPDATED_AT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "null_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('UPDATED_AT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('UPDATED_AT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Null Count", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.UPDATED_AT.null_percent", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "UPDATED_AT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "null_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('UPDATED_AT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('UPDATED_AT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Null Percent", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.source_generic_test_on_column_validation_any_type_column_anomalies_validation_null_count_int.13c361724d", "elementary_unique_id": "test.elementary_integration_tests.source_generic_test_on_column_validation_any_type_column_anomalies_validation_null_count_int.13c361724d.null_count_int.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "null_count_int", "test_name": "generic_test_on_column", "test_display_name": "Generic Test On Column", "latest_run_time": "2023-05-29T18:01:12+03:00", "latest_run_time_utc": "2023-05-29T15:01:12+00:00", "latest_run_status": "fail", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "dbt_test", "test_sub_type": "generic", "test_query": "with nothing as (select 1 as num)\n select * from nothing where num = 1", "test_params": {"column_name": "null_count_int", "model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": "Got 1 result, configured to fail if != 0", "result_query": "with nothing as (select 1 as num)\n select * from nothing where num = 1"}, "configuration": {"test_name": "generic_test_on_column", "test_params": {"column_name": "null_count_int", "model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}"}}}, "test_results": {"display_name": "generic_test_on_column", "results_sample": [{"num": 1.0}], "error_message": "Got 1 result, configured to fail if != 0", "failed_rows_count": 1}}], "source.elementary_integration_tests.training.any_type_column_anomalies_training": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_any_type_column_anomalies_training_occurred_at.50c4b3e11b", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_any_type_column_anomalies_training_occurred_at.50c4b3e11b.None.event_freshness", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_training", "column_name": null, "test_name": "event_freshness_anomalies", "test_display_name": "Event Freshness Anomalies", "latest_run_time": "2023-05-29T18:01:20+03:00", "latest_run_time_utc": "2023-05-29T15:01:20+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.training.any_type_column_anomalies_training", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_training", "test_type": "anomaly_detection", "test_sub_type": "event_freshness", "test_query": "select * from (\n \n\n \n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n\n \n \n \n \n\n \n \n\n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n \n\n \n\n \n \n \n\n\n \n \n \n\n \n \n \n\n \n \n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_50C4B3E11B_ELEMENTARY_SOURCE_EVENT_FRESHNESS_ANOMALIES_TRAINING_ANY_TYPE_COLUMN_ANOMALIES_TRAINING_OCCURRED_AT__ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n\n \n\n\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_TRAINING' as varchar)) and\n metric_name = cast('event_freshness' as varchar)", "test_params": {"event_timestamp_column": "occurred_at", "model": "{{ get_where_subquery(source('training', 'any_type_column_anomalies_training')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n \n\n \n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n\n \n \n \n \n\n \n \n\n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n \n\n \n\n \n \n \n\n\n \n \n \n\n \n \n \n\n \n \n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_50C4B3E11B_ELEMENTARY_SOURCE_EVENT_FRESHNESS_ANOMALIES_TRAINING_ANY_TYPE_COLUMN_ANOMALIES_TRAINING_OCCURRED_AT__ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n\n \n\n\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_TRAINING' as varchar)) and\n metric_name = cast('event_freshness' as varchar)"}, "configuration": {"test_name": "event_freshness_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Event Freshness", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_any_type_column_anomalies_training_.5733415eda", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_any_type_column_anomalies_training_.5733415eda.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_training", "column_name": null, "test_name": "freshness_anomalies", "test_display_name": "Freshness Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "error", "model_unique_id": "source.elementary_integration_tests.training.any_type_column_anomalies_training", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_training", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"model": "{{ get_where_subquery(source('training', 'any_type_column_anomalies_training')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": "Compilation Error in test elementary_source_freshness_anomalies_training_any_type_column_anomalies_training_ (models/schema.yml)\n freshness_anomalies test is not supported without timestamp_column.\n \n > in macro freshness_metric_query (macros/edr/data_monitoring/monitors_query/table_monitoring_query.sql)\n > called by macro get_metric_query (macros/edr/data_monitoring/monitors_query/table_monitoring_query.sql)\n > called by macro get_unified_metrics_query (macros/edr/data_monitoring/monitors_query/table_monitoring_query.sql)\n > called by macro table_monitoring_query (macros/edr/data_monitoring/monitors_query/table_monitoring_query.sql)\n > called by macro test_table_anomalies (macros/edr/tests/test_table_anomalies.sql)\n > called by macro test_freshness_anomalies (macros/edr/tests/test_freshness_anomalies.sql)\n > called by test elementary_source_freshness_anomalies_training_any_type_column_anomalies_training_ (models/schema.yml)", "result_query": null}, "configuration": {"test_name": "freshness_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": "Compilation Error in test elementary_source_freshness_anomalies_training_any_type_column_anomalies_training_ (models/schema.yml)\n freshness_anomalies test is not supported without timestamp_column.\n \n > in macro freshness_metric_query (macros/edr/data_monitoring/monitors_query/table_monitoring_query.sql)\n > called by macro get_metric_query (macros/edr/data_monitoring/monitors_query/table_monitoring_query.sql)\n > called by macro get_unified_metrics_query (macros/edr/data_monitoring/monitors_query/table_monitoring_query.sql)\n > called by macro table_monitoring_query (macros/edr/data_monitoring/monitors_query/table_monitoring_query.sql)\n > called by macro test_table_anomalies (macros/edr/tests/test_table_anomalies.sql)\n > called by macro test_freshness_anomalies (macros/edr/tests/test_freshness_anomalies.sql)\n > called by test elementary_source_freshness_anomalies_training_any_type_column_anomalies_training_ (models/schema.yml)"}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_any_type_column_anomalies_training_.e6ec3c50e5", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_any_type_column_anomalies_training_.e6ec3c50e5.None.row_count", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_training", "column_name": null, "test_name": "volume_anomalies", "test_display_name": "Volume Anomalies", "latest_run_time": "2023-05-29T18:01:20+03:00", "latest_run_time_utc": "2023-05-29T15:01:20+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.training.any_type_column_anomalies_training", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_training", "test_type": "anomaly_detection", "test_sub_type": "row_count", "test_query": "select * from (\n \n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n\n \n \n \n \n\n \n \n\n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n \n\n \n\n \n \n \n\n\n \n \n \n\n \n \n \n\n \n \n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_E6EC3C50E5_ELEMENTARY_SOURCE_VOLUME_ANOMALIES_TRAINING_ANY_TYPE_COLUMN_ANOMALIES_TRAINING___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n\n \n\n\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_TRAINING' as varchar)) and\n metric_name = cast('row_count' as varchar)", "test_params": {"model": "{{ get_where_subquery(source('training', 'any_type_column_anomalies_training')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n \n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n\n \n \n \n \n\n \n \n\n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n \n\n \n\n \n \n \n\n\n \n \n \n\n \n \n \n\n \n \n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_E6EC3C50E5_ELEMENTARY_SOURCE_VOLUME_ANOMALIES_TRAINING_ANY_TYPE_COLUMN_ANOMALIES_TRAINING___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n\n \n\n\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_TRAINING' as varchar)) and\n metric_name = cast('row_count' as varchar)"}, "configuration": {"test_name": "volume_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Row Count", "metrics": [], "result_description": "Not enough data to calculate anomaly score."}}], "source.elementary_integration_tests.training.string_column_anomalies_training": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a.None.event_freshness", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "string_column_anomalies_training", "column_name": null, "test_name": "event_freshness_anomalies", "test_display_name": "Event Freshness Anomalies", "latest_run_time": "2023-05-29T18:01:20+03:00", "latest_run_time_utc": "2023-05-29T15:01:20+00:00", "latest_run_status": "fail", "model_unique_id": "source.elementary_integration_tests.training.string_column_anomalies_training", "table_unique_id": "elementary_tests.test_seeds.string_column_anomalies_training", "test_type": "anomaly_detection", "test_sub_type": "event_freshness", "test_query": "select * from (\n \n\n \n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n\n \n \n \n \n\n \n \n\n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n \n\n \n\n \n \n \n\n\n \n \n \n\n \n \n \n\n \n \n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_4B2FB7183A_ELEMENTARY_SOURCE_EVENT_FRESHNESS_ANOMALIES_TRAINING_STRING_COLUMN_ANOMALIES_TRAINING_OCCURRED_AT__UPDATED_AT__ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n\n \n\n\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING' as varchar)) and\n metric_name = cast('event_freshness' as varchar)", "test_params": {"event_timestamp_column": "occurred_at", "update_timestamp_column": "updated_at", "model": "{{ get_where_subquery(source('training', 'string_column_anomalies_training')) }}", "sensitivity": 3, "timestamp_column": "updated_at", "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "The last event_freshness value is 86400.000. The average for this metric is 6360.000.", "result_query": "select * from (\n \n\n \n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n\n \n \n \n \n\n \n \n\n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n \n\n \n\n \n \n \n\n\n \n \n \n\n \n \n \n\n \n \n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_4B2FB7183A_ELEMENTARY_SOURCE_EVENT_FRESHNESS_ANOMALIES_TRAINING_STRING_COLUMN_ANOMALIES_TRAINING_OCCURRED_AT__UPDATED_AT__ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n\n \n\n\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING' as varchar)) and\n metric_name = cast('event_freshness' as varchar)"}, "configuration": {"test_name": "event_freshness_anomalies", "timestamp_column": "updated_at", "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Event Freshness", "metrics": [{"value": 3600.0, "average": 3600.0, "min_value": 3600.0, "max_value": 3600.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "976626400148e2cdbb44bba60f4c8976", "metric_id": "61fbf2e17f854b1905a1a63d7eee441e", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "test_unique_id": "test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "detected_at": "2023-05-29T15:01:19.696000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "event_freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-02 23:00:00.000", "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 3600.0, "min_metric_value": 3600.0, "max_metric_value": 3600.0, "training_avg": 3600.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last event_freshness value is 3600.000. The average for this metric is 3600.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 3600.0, "average": 3600.0, "min_value": 3600.0, "max_value": 3600.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "a337589241b5c57e8c6ad044b1d00a79", "metric_id": "9c17039bd7d1a0ae3b13ab8fe611a147", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "test_unique_id": "test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "detected_at": "2023-05-29T15:01:19.696000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "event_freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-03 23:00:00.000", "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 3600.0, "min_metric_value": 3600.0, "max_metric_value": 3600.0, "training_avg": 3600.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last event_freshness value is 3600.000. The average for this metric is 3600.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 3600.0, "average": 3600.0, "min_value": 3600.0, "max_value": 3600.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "05030703e58ce80a24bf45c37949c47f", "metric_id": "091ae1c9891077e49d2b7420e6c13b58", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "test_unique_id": "test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "detected_at": "2023-05-29T15:01:19.696000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "event_freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-04 23:00:00.000", "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 3600.0, "min_metric_value": 3600.0, "max_metric_value": 3600.0, "training_avg": 3600.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last event_freshness value is 3600.000. The average for this metric is 3600.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 3600.0, "average": 3600.0, "min_value": 3600.0, "max_value": 3600.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "a0d80ee44516135c05fd45b56e7c27db", "metric_id": "a908e96f16d03041b47244199cf8fbab", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "test_unique_id": "test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "detected_at": "2023-05-29T15:01:19.696000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "event_freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-05 23:00:00.000", "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 3600.0, "min_metric_value": 3600.0, "max_metric_value": 3600.0, "training_avg": 3600.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last event_freshness value is 3600.000. The average for this metric is 3600.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 3600.0, "average": 3600.0, "min_value": 3600.0, "max_value": 3600.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "b57688db6d8dcc6faa11f9674b5db618", "metric_id": "aacfefb6284c0218c1936484afc9fdbb", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "test_unique_id": "test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "detected_at": "2023-05-29T15:01:19.696000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "event_freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-06 23:00:00.000", "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 3600.0, "min_metric_value": 3600.0, "max_metric_value": 3600.0, "training_avg": 3600.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last event_freshness value is 3600.000. The average for this metric is 3600.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 3600.0, "average": 3600.0, "min_value": 3600.0, "max_value": 3600.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "95eda2e69daaa6ecd12fd080f2b8d9a5", "metric_id": "8a9c2b904f1382b3db9ab050f95d9150", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "test_unique_id": "test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "detected_at": "2023-05-29T15:01:19.696000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "event_freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-07 23:00:00.000", "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 3600.0, "min_metric_value": 3600.0, "max_metric_value": 3600.0, "training_avg": 3600.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last event_freshness value is 3600.000. The average for this metric is 3600.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 3600.0, "average": 3600.0, "min_value": 3600.0, "max_value": 3600.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "72b9b48f3fa91741af8477841fbea4ed", "metric_id": "34ca8e374daee1535abfc5d17b318a2c", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "test_unique_id": "test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "detected_at": "2023-05-29T15:01:19.696000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "event_freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-08 23:00:00.000", "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 3600.0, "min_metric_value": 3600.0, "max_metric_value": 3600.0, "training_avg": 3600.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last event_freshness value is 3600.000. The average for this metric is 3600.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 3600.0, "average": 3600.0, "min_value": 3600.0, "max_value": 3600.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "81865e9c08d5c8594de810a56a2a949e", "metric_id": "492da2c4232eac546b3ceb2f442f119d", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "test_unique_id": "test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "detected_at": "2023-05-29T15:01:19.696000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "event_freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-09 23:00:00.000", "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 3600.0, "min_metric_value": 3600.0, "max_metric_value": 3600.0, "training_avg": 3600.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last event_freshness value is 3600.000. The average for this metric is 3600.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 3600.0, "average": 3600.0, "min_value": 3600.0, "max_value": 3600.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "e878bc0c332077de4fe1b2c8f9611cea", "metric_id": "0cdf8169aa72b4f86303a7b45b8385da", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "test_unique_id": "test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "detected_at": "2023-05-29T15:01:19.696000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "event_freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-10 23:00:00.000", "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 3600.0, "min_metric_value": 3600.0, "max_metric_value": 3600.0, "training_avg": 3600.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last event_freshness value is 3600.000. The average for this metric is 3600.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 3600.0, "average": 3600.0, "min_value": 3600.0, "max_value": 3600.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "3aed56e6941a6b7093dec0d906e8b5fe", "metric_id": "19fc8628bf56728037fc33790c5f4f1e", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "test_unique_id": "test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "detected_at": "2023-05-29T15:01:19.696000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "event_freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-11 23:00:00.000", "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 3600.0, "min_metric_value": 3600.0, "max_metric_value": 3600.0, "training_avg": 3600.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last event_freshness value is 3600.000. The average for this metric is 3600.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 3600.0, "average": 3600.0, "min_value": 3600.0, "max_value": 3600.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "fba7dc3d93a1a7e848e802690aec1ade", "metric_id": "cb050ba27d613826d2b9432d4b456ab7", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "test_unique_id": "test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "detected_at": "2023-05-29T15:01:19.696000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "event_freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-12 23:00:00.000", "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 3600.0, "min_metric_value": 3600.0, "max_metric_value": 3600.0, "training_avg": 3600.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last event_freshness value is 3600.000. The average for this metric is 3600.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 3600.0, "average": 3600.0, "min_value": 3600.0, "max_value": 3600.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "9242593e79dfc5e08d246514784036ae", "metric_id": "c4d0b2afc3421710727349d600a87787", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "test_unique_id": "test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "detected_at": "2023-05-29T15:01:19.696000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "event_freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-13 23:00:00.000", "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 3600.0, "min_metric_value": 3600.0, "max_metric_value": 3600.0, "training_avg": 3600.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last event_freshness value is 3600.000. The average for this metric is 3600.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 3600.0, "average": 3600.0, "min_value": 3600.0, "max_value": 3600.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "eb1622b6e6411e34206a37adb208adbd", "metric_id": "d7916edf453fcb13d92d98c9e2249367", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "test_unique_id": "test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "detected_at": "2023-05-29T15:01:19.696000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "event_freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-14 23:00:00.000", "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 3600.0, "min_metric_value": 3600.0, "max_metric_value": 3600.0, "training_avg": 3600.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last event_freshness value is 3600.000. The average for this metric is 3600.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 3600.0, "average": 3600.0, "min_value": 3600.0, "max_value": 3600.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "3f7275d1a4224a6c6cafd339c92a4435", "metric_id": "984d53c99692c73009d3fd9fbbc43a71", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "test_unique_id": "test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "detected_at": "2023-05-29T15:01:19.696000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "event_freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-15 23:00:00.000", "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 3600.0, "min_metric_value": 3600.0, "max_metric_value": 3600.0, "training_avg": 3600.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last event_freshness value is 3600.000. The average for this metric is 3600.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 3600.0, "average": 3600.0, "min_value": 3600.0, "max_value": 3600.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "2bcc0952b1c62d454d2c3bea98768940", "metric_id": "8f94c6af2b4c349fe850db2c865e03bd", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "test_unique_id": "test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "detected_at": "2023-05-29T15:01:19.696000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "event_freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-16 23:00:00.000", "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 3600.0, "min_metric_value": 3600.0, "max_metric_value": 3600.0, "training_avg": 3600.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last event_freshness value is 3600.000. The average for this metric is 3600.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 3600.0, "average": 3600.0, "min_value": 3600.0, "max_value": 3600.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "d31133b8758e360f07db0679e1e1c081", "metric_id": "55ccd3536677270ad246c72c42573bbf", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "test_unique_id": "test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "detected_at": "2023-05-29T15:01:19.696000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "event_freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-17 23:00:00.000", "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 3600.0, "min_metric_value": 3600.0, "max_metric_value": 3600.0, "training_avg": 3600.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last event_freshness value is 3600.000. The average for this metric is 3600.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 3600.0, "average": 3600.0, "min_value": 3600.0, "max_value": 3600.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "8d2372db2c71e4d55ddac4b4adde14ea", "metric_id": "e723794b4999e1bcabdd7cabcfd67b44", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "test_unique_id": "test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "detected_at": "2023-05-29T15:01:19.696000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "event_freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-18 23:00:00.000", "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 3600.0, "min_metric_value": 3600.0, "max_metric_value": 3600.0, "training_avg": 3600.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last event_freshness value is 3600.000. The average for this metric is 3600.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 3600.0, "average": 3600.0, "min_value": 3600.0, "max_value": 3600.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "e3eb7d980c178166375cc2b2b0130b10", "metric_id": "5ca062022f19683e5b69f817ccd7e9ef", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "test_unique_id": "test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "detected_at": "2023-05-29T15:01:19.696000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "event_freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-19 23:00:00.000", "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 3600.0, "min_metric_value": 3600.0, "max_metric_value": 3600.0, "training_avg": 3600.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last event_freshness value is 3600.000. The average for this metric is 3600.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 3600.0, "average": 3600.0, "min_value": 3600.0, "max_value": 3600.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "ea92d7f8c9db2ed043bbed1d7886337d", "metric_id": "6b97045f86e8e2df3d0e1b99a2b65594", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "test_unique_id": "test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "detected_at": "2023-05-29T15:01:19.696000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "event_freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-20 23:00:00.000", "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 3600.0, "min_metric_value": 3600.0, "max_metric_value": 3600.0, "training_avg": 3600.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last event_freshness value is 3600.000. The average for this metric is 3600.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 3600.0, "average": 3600.0, "min_value": 3600.0, "max_value": 3600.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "f93f62e292358acd2aa44adc7af959e6", "metric_id": "1a993346cb3cc36f6ad0d8bc057dea9b", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "test_unique_id": "test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "detected_at": "2023-05-29T15:01:19.696000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "event_freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-21 23:00:00.000", "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 3600.0, "min_metric_value": 3600.0, "max_metric_value": 3600.0, "training_avg": 3600.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last event_freshness value is 3600.000. The average for this metric is 3600.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 3600.0, "average": 3600.0, "min_value": 3600.0, "max_value": 3600.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "b789ffe653ab5ece0160d44eb197c896", "metric_id": "3becfda3e1e9edfb74e52d370414cd12", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "test_unique_id": "test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "detected_at": "2023-05-29T15:01:19.696000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "event_freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-22 23:00:00.000", "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 3600.0, "min_metric_value": 3600.0, "max_metric_value": 3600.0, "training_avg": 3600.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last event_freshness value is 3600.000. The average for this metric is 3600.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 3600.0, "average": 3600.0, "min_value": 3600.0, "max_value": 3600.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "79a070a525625310b4f5988d0d71523c", "metric_id": "5cfdb156c177e7e0435518e13608a350", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "test_unique_id": "test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "detected_at": "2023-05-29T15:01:19.696000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "event_freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-23 23:00:00.000", "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 3600.0, "min_metric_value": 3600.0, "max_metric_value": 3600.0, "training_avg": 3600.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last event_freshness value is 3600.000. The average for this metric is 3600.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 3600.0, "average": 3600.0, "min_value": 3600.0, "max_value": 3600.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "acacd93148a180f6d4647e010a4b6abe", "metric_id": "b0b5cacc0f9ecb4baf5abce4c1d2d252", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "test_unique_id": "test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "detected_at": "2023-05-29T15:01:19.696000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "event_freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-24 23:00:00.000", "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 3600.0, "min_metric_value": 3600.0, "max_metric_value": 3600.0, "training_avg": 3600.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last event_freshness value is 3600.000. The average for this metric is 3600.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 3600.0, "average": 3600.0, "min_value": 3600.0, "max_value": 3600.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "05892d9526b37cf97889548c86ebffee", "metric_id": "ceac36028554de8d6edcd355811db263", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "test_unique_id": "test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "detected_at": "2023-05-29T15:01:19.696000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "event_freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-25 23:00:00.000", "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 3600.0, "min_metric_value": 3600.0, "max_metric_value": 3600.0, "training_avg": 3600.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last event_freshness value is 3600.000. The average for this metric is 3600.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 3600.0, "average": 3600.0, "min_value": 3600.0, "max_value": 3600.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "3ff23310e1db592d6d4596c0f345fb82", "metric_id": "a855aacd0efe4b0efb379374d0b26b38", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "test_unique_id": "test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "detected_at": "2023-05-29T15:01:19.696000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "event_freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-26 23:00:00.000", "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 3600.0, "min_metric_value": 3600.0, "max_metric_value": 3600.0, "training_avg": 3600.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last event_freshness value is 3600.000. The average for this metric is 3600.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 3600.0, "average": 3600.0, "min_value": 3600.0, "max_value": 3600.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "0c278f4796592014e5a02f7b3c4320a9", "metric_id": "4aebb8280164971713264e66f9c425a9", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "test_unique_id": "test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "detected_at": "2023-05-29T15:01:19.696000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "event_freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-27 23:00:00.000", "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 3600.0, "min_metric_value": 3600.0, "max_metric_value": 3600.0, "training_avg": 3600.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last event_freshness value is 3600.000. The average for this metric is 3600.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 3600.0, "average": 3600.0, "min_value": 3600.0, "max_value": 3600.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "352774aeecccf931228f7c6133c67346", "metric_id": "67a342e4f8828ade1cdbb727ac7026af", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "test_unique_id": "test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "detected_at": "2023-05-29T15:01:19.696000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "event_freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-28 23:00:00.000", "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 3600.0, "min_metric_value": 3600.0, "max_metric_value": 3600.0, "training_avg": 3600.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last event_freshness value is 3600.000. The average for this metric is 3600.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 3600.0, "average": 3600.0, "min_value": 3600.0, "max_value": 3600.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "1b264baf60e8f4d1eaaef5e8fbedffbf", "metric_id": "c4cf975ecaa40b772e6a3e29fd3d7084", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "test_unique_id": "test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "detected_at": "2023-05-29T15:01:19.696000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "event_freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-29 23:00:00.000", "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 3600.0, "min_metric_value": 3600.0, "max_metric_value": 3600.0, "training_avg": 3600.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last event_freshness value is 3600.000. The average for this metric is 3600.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 86400.0, "average": 6360.0, "min_value": 3600.0, "max_value": 3600.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "7961bf35850702ab0805801d104c875a", "metric_id": "e8e7dfbe70e56bdda85063307f60c173", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "test_unique_id": "test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "detected_at": "2023-05-29T15:01:19.696000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "event_freshness", "anomaly_score": 5.294651389216606, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 86400.0, "min_metric_value": -38991.42776142775, "max_metric_value": 51711.42776142775, "training_avg": 6360.0, "training_stddev": 15117.142587142585, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last event_freshness value is 86400.000. The average for this metric is 6360.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": true}], "result_description": "The last event_freshness value is 86400.000. The average for this metric is 6360.000."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af.None.freshness", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "string_column_anomalies_training", "column_name": null, "test_name": "freshness_anomalies", "test_display_name": "Freshness Anomalies", "latest_run_time": "2023-05-29T18:01:20+03:00", "latest_run_time_utc": "2023-05-29T15:01:20+00:00", "latest_run_status": "fail", "model_unique_id": "source.elementary_integration_tests.training.string_column_anomalies_training", "table_unique_id": "elementary_tests.test_seeds.string_column_anomalies_training", "test_type": "anomaly_detection", "test_sub_type": "freshness", "test_query": "select * from (\n \n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n\n \n \n \n \n\n \n \n\n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n \n\n \n\n \n \n \n\n\n \n \n \n\n \n \n \n\n \n \n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_4640CC98AF_ELEMENTARY_SOURCE_FRESHNESS_ANOMALIES_TRAINING_STRING_COLUMN_ANOMALIES_TRAINING___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n\n \n\n\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING' as varchar)) and\n metric_name = cast('freshness' as varchar)", "test_params": {"model": "{{ get_where_subquery(source('training', 'string_column_anomalies_training')) }}", "sensitivity": 3, "timestamp_column": "updated_at", "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Last update was at 1969-12-30 00:00:00.000, 48.00 hours ago. Usually the table is updated within 24.80 hours.", "result_query": "select * from (\n \n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n\n \n \n \n \n\n \n \n\n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n \n\n \n\n \n \n \n\n\n \n \n \n\n \n \n \n\n \n \n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_4640CC98AF_ELEMENTARY_SOURCE_FRESHNESS_ANOMALIES_TRAINING_STRING_COLUMN_ANOMALIES_TRAINING___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n\n \n\n\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING' as varchar)) and\n metric_name = cast('freshness' as varchar)"}, "configuration": {"test_name": "freshness_anomalies", "timestamp_column": "updated_at", "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Freshness", "metrics": [{"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "5a2c379a370ece3be09a902a045e4347", "metric_id": "b017591b4691b175751ef3858d78947b", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-05-29T15:01:19.742000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-03 00:00:00.000", "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-03 00:00:00.000, 24.00 hours ago. Usually the table is updated within 24.00 hours.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "7f04880e23699e1d8b07c5213ad2f5c7", "metric_id": "373efab52c66804d6e5075467fe9ac7e", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-05-29T15:01:19.742000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-04 00:00:00.000", "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-04 00:00:00.000, 24.00 hours ago. Usually the table is updated within 24.00 hours.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "4a479fb26befc252a751964068f3ef95", "metric_id": "d534ed59a6c741e841f1e33f0739a794", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-05-29T15:01:19.742000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-05 00:00:00.000", "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-05 00:00:00.000, 24.00 hours ago. Usually the table is updated within 24.00 hours.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "cc41c270ede2173779ad29bc5ba0ea79", "metric_id": "0d161842190c6c62f7d42667a838f9a3", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-05-29T15:01:19.742000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-06 00:00:00.000", "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-06 00:00:00.000, 24.00 hours ago. Usually the table is updated within 24.00 hours.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "bb2c59f9f99101d710bb4c8e21490b81", "metric_id": "b0b22c9e3b6d2a69fc676ffc1d21c6cd", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-05-29T15:01:19.742000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-07 00:00:00.000", "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-07 00:00:00.000, 24.00 hours ago. Usually the table is updated within 24.00 hours.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "27bfc2b5f202931f0f5e213a73d691ce", "metric_id": "ca9c0294d9bd356cf29bb4b8f924f91a", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-05-29T15:01:19.742000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-08 00:00:00.000", "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-08 00:00:00.000, 24.00 hours ago. Usually the table is updated within 24.00 hours.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "81d8d957f70c825076d8f88fc1f4eb32", "metric_id": "953a8a3ea2d252849615aeba76a85c17", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-05-29T15:01:19.742000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-09 00:00:00.000", "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-09 00:00:00.000, 24.00 hours ago. Usually the table is updated within 24.00 hours.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "5ed291ec4307e9b29ab5f9a811d70ac9", "metric_id": "7a49583c8f85da6687589a48ce5b9d36", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-05-29T15:01:19.742000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-10 00:00:00.000", "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-10 00:00:00.000, 24.00 hours ago. Usually the table is updated within 24.00 hours.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "987c7a1a19d1fbe8c491d2ad79350f64", "metric_id": "8d4ae36cc31dd1b5b1895b5712c2adb2", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-05-29T15:01:19.742000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-11 00:00:00.000", "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-11 00:00:00.000, 24.00 hours ago. Usually the table is updated within 24.00 hours.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "05142d4c4e3c732cfd8af0005391e1d7", "metric_id": "84d0e37dc7ef5be6c880ca761ede9a81", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-05-29T15:01:19.742000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-12 00:00:00.000", "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-12 00:00:00.000, 24.00 hours ago. Usually the table is updated within 24.00 hours.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "5d3e4c9cd55ba650b97c5bca114fc65e", "metric_id": "2732d56339e7e9f4e0fe41ba4b4de8d7", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-05-29T15:01:19.742000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-13 00:00:00.000", "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-13 00:00:00.000, 24.00 hours ago. Usually the table is updated within 24.00 hours.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "635bb817df0051c651c703175500ffc0", "metric_id": "95e3be4dbe34291d97f79d5d31b38d5e", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-05-29T15:01:19.742000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-14 00:00:00.000", "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-14 00:00:00.000, 24.00 hours ago. Usually the table is updated within 24.00 hours.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "1de67d04b1aadc3c059bfbc3cdcf4813", "metric_id": "c6e68592bece86a3bbb38d8ec5e5172b", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-05-29T15:01:19.742000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-15 00:00:00.000", "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-15 00:00:00.000, 24.00 hours ago. Usually the table is updated within 24.00 hours.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "87c82337cab9b99f2d631e4759171faa", "metric_id": "af65a96b449fc4e0154127a320400ee3", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-05-29T15:01:19.742000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-16 00:00:00.000", "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-16 00:00:00.000, 24.00 hours ago. Usually the table is updated within 24.00 hours.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "4a870d1d2a87e18452df3ce95af286c8", "metric_id": "38052bc67e455472727ff178e6dbcd07", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-05-29T15:01:19.742000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-17 00:00:00.000", "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-17 00:00:00.000, 24.00 hours ago. Usually the table is updated within 24.00 hours.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "dc2315f51a4c348bdaeac4957cf25494", "metric_id": "9b28367ada418af4baccefcf967a48ea", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-05-29T15:01:19.742000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-18 00:00:00.000", "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-18 00:00:00.000, 24.00 hours ago. Usually the table is updated within 24.00 hours.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "884303097b7dd6c9267b13f83ec08481", "metric_id": "06ef252f0825d19c1522452b8a75bdc7", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-05-29T15:01:19.742000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-19 00:00:00.000", "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-19 00:00:00.000, 24.00 hours ago. Usually the table is updated within 24.00 hours.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "2e3f2b340216aca4053094b6b4d35113", "metric_id": "01b49c0a350a2600845cdc5084af9ca6", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-05-29T15:01:19.742000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-20 00:00:00.000", "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-20 00:00:00.000, 24.00 hours ago. Usually the table is updated within 24.00 hours.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "593378401cbd26c250d1e5fe93134eea", "metric_id": "7a05d96e593d0471e363cf04b63b6a89", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-05-29T15:01:19.742000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-21 00:00:00.000", "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-21 00:00:00.000, 24.00 hours ago. Usually the table is updated within 24.00 hours.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "5095ef4c599fccdba84b28b1253c6918", "metric_id": "a2548ea13c13b3c3f07d1b323ec13746", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-05-29T15:01:19.742000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-22 00:00:00.000", "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-22 00:00:00.000, 24.00 hours ago. Usually the table is updated within 24.00 hours.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "aebe7470d6040982ad863373451d0163", "metric_id": "289415c75c8a7c10667961977cae3575", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-05-29T15:01:19.742000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-23 00:00:00.000", "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-23 00:00:00.000, 24.00 hours ago. Usually the table is updated within 24.00 hours.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "c02e3b6b535424e70988515edcc688c5", "metric_id": "21540ad3df39e85cb000cb10541a43cc", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-05-29T15:01:19.742000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-24 00:00:00.000", "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-24 00:00:00.000, 24.00 hours ago. Usually the table is updated within 24.00 hours.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "75a72cb04174e2baf47cbb9829863812", "metric_id": "f84e3bc03dd0627bbc76d409605eb126", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-05-29T15:01:19.742000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-25 00:00:00.000", "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-25 00:00:00.000, 24.00 hours ago. Usually the table is updated within 24.00 hours.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "01a0ebd7ba1b9f1fbdd1cfea098ccbb2", "metric_id": "907e072702cbc9e796aafa54f2e5bcf2", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-05-29T15:01:19.742000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-26 00:00:00.000", "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-26 00:00:00.000, 24.00 hours ago. Usually the table is updated within 24.00 hours.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "f34876cc70d6ea9dd571d219d3dff47f", "metric_id": "8bbd850dc17814d0301fb3bd018f87ba", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-05-29T15:01:19.742000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-27 00:00:00.000", "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-27 00:00:00.000, 24.00 hours ago. Usually the table is updated within 24.00 hours.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "78ddc319bdbd92b6172b4511eed8399d", "metric_id": "e994a58376fb1c2857b359829677c3df", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-05-29T15:01:19.742000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-28 00:00:00.000", "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-28 00:00:00.000, 24.00 hours ago. Usually the table is updated within 24.00 hours.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "92b932a4e0653c0b00434303f8b8e4a3", "metric_id": "84efbfcc5cb6a1be6c89ab3322dc3ef2", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-05-29T15:01:19.742000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-29 00:00:00.000", "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-29 00:00:00.000, 24.00 hours ago. Usually the table is updated within 24.00 hours.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 86400.0, "average": 86400.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "ff1652667714d91a0fd818ceead4f49c", "metric_id": "b18573e920f378858d7d57b33c2fdc26", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-05-29T15:01:19.742000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-30 00:00:00.000", "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 86400.0, "min_metric_value": 86400.0, "max_metric_value": 86400.0, "training_avg": 86400.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-30 00:00:00.000, 24.00 hours ago. Usually the table is updated within 24.00 hours.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 172800.0, "average": 89280.0, "min_value": 86400.0, "max_value": 86400.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "a6f0d1a6e7504f7e7e9485f913811c6e", "metric_id": "591c0ca3719b3ffc337a05ae0e449099", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "detected_at": "2023-05-29T15:01:19.742000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "freshness", "anomaly_score": 5.294651389216606, "anomaly_score_threshold": 3.0, "anomalous_value": "1969-12-30 00:00:00.000", "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 172800.0, "min_metric_value": 41956.771031553646, "max_metric_value": 136603.22896844635, "training_avg": 89280.0, "training_stddev": 15774.409656148784, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "Last update was at 1969-12-30 00:00:00.000, 48.00 hours ago. Usually the table is updated within 24.80 hours.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": true}], "result_description": "Last update was at 1969-12-30 00:00:00.000, 48.00 hours ago. Usually the table is updated within 24.80 hours."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5.None.row_count", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "string_column_anomalies_training", "column_name": null, "test_name": "volume_anomalies", "test_display_name": "Volume Anomalies", "latest_run_time": "2023-05-29T18:01:20+03:00", "latest_run_time_utc": "2023-05-29T15:01:20+00:00", "latest_run_status": "fail", "model_unique_id": "source.elementary_integration_tests.training.string_column_anomalies_training", "table_unique_id": "elementary_tests.test_seeds.string_column_anomalies_training", "test_type": "anomaly_detection", "test_sub_type": "row_count", "test_query": "select * from (\n \n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n\n \n \n \n \n\n \n \n\n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n \n\n \n\n \n \n \n\n\n \n \n \n\n \n \n \n\n \n \n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_F934F558B5_ELEMENTARY_SOURCE_VOLUME_ANOMALIES_TRAINING_STRING_COLUMN_ANOMALIES_TRAINING___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n\n \n\n\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING' as varchar)) and\n metric_name = cast('row_count' as varchar)", "test_params": {"model": "{{ get_where_subquery(source('training', 'string_column_anomalies_training')) }}", "sensitivity": 3, "timestamp_column": "updated_at", "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "The last row_count value is 0.000. The average for this metric is 96.667.", "result_query": "select * from (\n \n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n\n \n \n \n \n\n \n \n\n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n \n\n \n\n \n \n \n\n\n \n \n \n\n \n \n \n\n \n \n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_F934F558B5_ELEMENTARY_SOURCE_VOLUME_ANOMALIES_TRAINING_STRING_COLUMN_ANOMALIES_TRAINING___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n\n \n\n\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING' as varchar)) and\n metric_name = cast('row_count' as varchar)"}, "configuration": {"test_name": "volume_anomalies", "timestamp_column": "updated_at", "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_results": {"display_name": "Row Count", "metrics": [{"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-03T00:00:00", "end_time": "1969-12-04T00:00:00", "id": "ec1a7dd2e0b1b2ec523882978ad763e4", "metric_id": "6a9da885e82ac458f177b9105c1be707", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-05-29T15:01:19.724000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-03T00:00:00", "bucket_end": "1969-12-04T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 2.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-04T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100.000. The average for this metric is 100.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-04T00:00:00", "end_time": "1969-12-05T00:00:00", "id": "7f0a46955bb83e4ba14952cc3d6b7c4f", "metric_id": "7473fb42feb489a9b90a6c0ddf1307af", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-05-29T15:01:19.724000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-04T00:00:00", "bucket_end": "1969-12-05T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 3.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-05T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100.000. The average for this metric is 100.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-05T00:00:00", "end_time": "1969-12-06T00:00:00", "id": "a5e03678baab38019c6822c49b5ed472", "metric_id": "fe9d41b90d753691678905fe3a6b3ee7", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-05-29T15:01:19.724000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-05T00:00:00", "bucket_end": "1969-12-06T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 4.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-06T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100.000. The average for this metric is 100.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-06T00:00:00", "end_time": "1969-12-07T00:00:00", "id": "976aece1f303fe9b961bdade63d53960", "metric_id": "9c429dfc860bc3b834cb65d336a1c28d", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-05-29T15:01:19.724000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-06T00:00:00", "bucket_end": "1969-12-07T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 5.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-07T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100.000. The average for this metric is 100.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-07T00:00:00", "end_time": "1969-12-08T00:00:00", "id": "1fa01e9a5506ba49af3f72ff132e1110", "metric_id": "81622b942b038aa72531ff512050e22a", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-05-29T15:01:19.724000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-07T00:00:00", "bucket_end": "1969-12-08T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 6.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-08T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100.000. The average for this metric is 100.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-08T00:00:00", "end_time": "1969-12-09T00:00:00", "id": "6d7d7573265ae202c17e4edf3986e5ea", "metric_id": "044a4420aba8ec9ea6fe6a9ca7225a27", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-05-29T15:01:19.724000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-08T00:00:00", "bucket_end": "1969-12-09T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 7.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-09T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100.000. The average for this metric is 100.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-09T00:00:00", "end_time": "1969-12-10T00:00:00", "id": "efbd0b176f1aea72ce77f4679c929ab1", "metric_id": "a8184eff3781392138456ba121c27e7f", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-05-29T15:01:19.724000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-09T00:00:00", "bucket_end": "1969-12-10T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 8.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-10T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100.000. The average for this metric is 100.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-10T00:00:00", "end_time": "1969-12-11T00:00:00", "id": "93554c56644a8c69f6e8e5e464dc1409", "metric_id": "3632b89525c23dc55059baff3bc52c81", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-05-29T15:01:19.724000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-10T00:00:00", "bucket_end": "1969-12-11T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 9.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-11T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100.000. The average for this metric is 100.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-11T00:00:00", "end_time": "1969-12-12T00:00:00", "id": "b5ccbb17efad898f75f8f626ef4d7c08", "metric_id": "b2c38517c90e14bc70d53912ebc51157", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-05-29T15:01:19.724000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-11T00:00:00", "bucket_end": "1969-12-12T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 10.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-12T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100.000. The average for this metric is 100.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-12T00:00:00", "end_time": "1969-12-13T00:00:00", "id": "d595c85607984c467e7b83c2a0f18e62", "metric_id": "ccfb352aaa2a2dd7532868a0db4a578b", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-05-29T15:01:19.724000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-12T00:00:00", "bucket_end": "1969-12-13T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 11.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-13T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100.000. The average for this metric is 100.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-13T00:00:00", "end_time": "1969-12-14T00:00:00", "id": "14a13c7ac2cc0b2c8d23a8cd239f2ce6", "metric_id": "c380cef8092f3dc0b6def7c38f060068", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-05-29T15:01:19.724000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-13T00:00:00", "bucket_end": "1969-12-14T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 12.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-14T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100.000. The average for this metric is 100.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-14T00:00:00", "end_time": "1969-12-15T00:00:00", "id": "45a07b85a1d306b99e0433e038b8bdcc", "metric_id": "f0b2da9876ab82099316bfb4ec4430ee", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-05-29T15:01:19.724000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-14T00:00:00", "bucket_end": "1969-12-15T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 13.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-15T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100.000. The average for this metric is 100.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-15T00:00:00", "end_time": "1969-12-16T00:00:00", "id": "c2047752031c2c6377230888f1c786a9", "metric_id": "2d5d783295ec1f5116a26244cd5600ed", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-05-29T15:01:19.724000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-15T00:00:00", "bucket_end": "1969-12-16T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 14.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-16T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100.000. The average for this metric is 100.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-16T00:00:00", "end_time": "1969-12-17T00:00:00", "id": "f4dff7511df86565a3a68f9208d9b6c0", "metric_id": "3fe02b39bf4a939c573f2daf1bc60a3a", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-05-29T15:01:19.724000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-16T00:00:00", "bucket_end": "1969-12-17T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 15.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-17T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100.000. The average for this metric is 100.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-17T00:00:00", "end_time": "1969-12-18T00:00:00", "id": "bed3daff2270a1f8c1f9bb5ada047ecd", "metric_id": "c8ddcda226d57cc62498d919489c90e3", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-05-29T15:01:19.724000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-17T00:00:00", "bucket_end": "1969-12-18T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 16.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-18T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100.000. The average for this metric is 100.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-18T00:00:00", "end_time": "1969-12-19T00:00:00", "id": "e14fa90534458a9e50c8219bba685567", "metric_id": "5f7befa11caacd57cac372f86713d6a8", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-05-29T15:01:19.724000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-18T00:00:00", "bucket_end": "1969-12-19T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 17.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-19T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100.000. The average for this metric is 100.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-19T00:00:00", "end_time": "1969-12-20T00:00:00", "id": "6c7fc64819f33759f58dd0e0d20ccf0b", "metric_id": "d8d6baeb4c6661ab2b8d8d85330cb0d8", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-05-29T15:01:19.724000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-19T00:00:00", "bucket_end": "1969-12-20T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 18.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-20T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100.000. The average for this metric is 100.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-20T00:00:00", "end_time": "1969-12-21T00:00:00", "id": "306c85c4eea5cd0fdab557e443fab7bb", "metric_id": "d448b8a90ebb9cb21451501aec2af241", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-05-29T15:01:19.724000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-20T00:00:00", "bucket_end": "1969-12-21T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 19.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-21T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100.000. The average for this metric is 100.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-21T00:00:00", "end_time": "1969-12-22T00:00:00", "id": "5125482418cc0f2e8c398621311f8ce0", "metric_id": "3c69203d82d79ffcab3892e6d07c3b85", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-05-29T15:01:19.724000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-21T00:00:00", "bucket_end": "1969-12-22T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 20.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-22T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100.000. The average for this metric is 100.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-22T00:00:00", "end_time": "1969-12-23T00:00:00", "id": "7474b6f29b969e64a15de36ab1f842fb", "metric_id": "f003f94a77fbb0092bea78af3ddaf6bf", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-05-29T15:01:19.724000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-22T00:00:00", "bucket_end": "1969-12-23T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 21.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-23T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100.000. The average for this metric is 100.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-23T00:00:00", "end_time": "1969-12-24T00:00:00", "id": "af2c979c13647cb7468369f76486171b", "metric_id": "a447b732ba0fe1556fb2271e91230ef0", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-05-29T15:01:19.724000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-23T00:00:00", "bucket_end": "1969-12-24T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 22.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-24T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100.000. The average for this metric is 100.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-24T00:00:00", "end_time": "1969-12-25T00:00:00", "id": "8957aafde45df7ccdb213b0cb0cff450", "metric_id": "ff483b4b532c5de928da50f99e0dba3f", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-05-29T15:01:19.724000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-24T00:00:00", "bucket_end": "1969-12-25T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 23.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-25T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100.000. The average for this metric is 100.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-25T00:00:00", "end_time": "1969-12-26T00:00:00", "id": "cbf8e15a52a1093a66f28e1fab545a71", "metric_id": "aa638efa6187b57b9d172dbacdce21f6", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-05-29T15:01:19.724000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-25T00:00:00", "bucket_end": "1969-12-26T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 24.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-26T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100.000. The average for this metric is 100.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-26T00:00:00", "end_time": "1969-12-27T00:00:00", "id": "8c5d55be7ea3ed3220bc3a64b0006524", "metric_id": "0f6745546abb850c183cd738949f9885", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-05-29T15:01:19.724000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-26T00:00:00", "bucket_end": "1969-12-27T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 25.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-27T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100.000. The average for this metric is 100.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-27T00:00:00", "end_time": "1969-12-28T00:00:00", "id": "5672fbbc5f012cb8c96430d23f54da03", "metric_id": "98fe4d1a552241ca667acacce19bf942", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-05-29T15:01:19.724000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-27T00:00:00", "bucket_end": "1969-12-28T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 26.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-28T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100.000. The average for this metric is 100.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-28T00:00:00", "end_time": "1969-12-29T00:00:00", "id": "4e1c95c4659e81b67f01f02d419c0d39", "metric_id": "3e2eebc31530375e1a7dcdee2a7da37d", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-05-29T15:01:19.724000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-28T00:00:00", "bucket_end": "1969-12-29T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 27.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-29T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100.000. The average for this metric is 100.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-29T00:00:00", "end_time": "1969-12-30T00:00:00", "id": "ec583f0aedb969c7b7862007bc72587e", "metric_id": "fa18335d803813e5aafda4fcc1cbbd4c", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-05-29T15:01:19.724000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-29T00:00:00", "bucket_end": "1969-12-30T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 28.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-30T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100.000. The average for this metric is 100.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 100.0, "average": 100.0, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-30T00:00:00", "end_time": "1969-12-31T00:00:00", "id": "d514f04c667a029382a97a2d2826c619", "metric_id": "9f0e208a532b0d2b3a5d724cf033d27e", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-05-29T15:01:19.724000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": 0.0, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-30T00:00:00", "bucket_end": "1969-12-31T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 100.0, "min_metric_value": 100.0, "max_metric_value": 100.0, "training_avg": 100.0, "training_stddev": 0.0, "training_set_size": 29.0, "training_start": "1969-12-03T00:00:00", "training_end": "1969-12-31T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 100.000. The average for this metric is 100.000.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": false}, {"value": 0.0, "average": 96.66666666666667, "min_value": 100.0, "max_value": 100.0, "start_time": "1969-12-31T00:00:00", "end_time": "1970-01-01T00:00:00", "id": "7911fde719eefb5359c2b88445e10bb7", "metric_id": "1410c80d490ea4805c0e6de93d1510cc", "test_execution_id": "8e3c93a6-77b4-4de0-9778-475945f3a741.test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "detected_at": "2023-05-29T15:01:19.724000", "full_table_name": "ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING", "column_name": null, "metric_name": "row_count", "anomaly_score": -5.2946513892166065, "anomaly_score_threshold": 3.0, "anomalous_value": null, "bucket_start": "1969-12-31T00:00:00", "bucket_end": "1970-01-01T00:00:00", "bucket_seasonality": "no_seasonality", "metric_value": 0.0, "min_metric_value": 41.894410916150065, "max_metric_value": 151.43892241718328, "training_avg": 96.66666666666667, "training_stddev": 18.257418583505537, "training_set_size": 30.0, "training_start": "1969-12-03T00:00:00", "training_end": "1970-01-01T00:00:00", "dimension": null, "dimension_value": null, "anomaly_description": "The last row_count value is 0.000. The average for this metric is 96.667.", "max_bucket_end": "1970-01-01T00:00:00", "is_anomalous": true}], "result_description": "The last row_count value is 0.000. The average for this metric is 96.667."}}], "model.elementary_integration_tests.users_per_day_weekly_seasonal": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_users_per_day_weekly_seasonal_14__2__updated_at.6a5e06fe3b", "elementary_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_users_per_day_weekly_seasonal_14__2__updated_at.6a5e06fe3b.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "users_per_day_weekly_seasonal", "column_name": null, "test_name": "volume_anomalies", "test_display_name": "Volume Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.users_per_day_weekly_seasonal", "table_unique_id": "elementary_tests.noya_tests.users_per_day_weekly_seasonal", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"timestamp_column": "updated_at", "sensitivity": 2, "backfill_days": 14, "model": "{{ get_where_subquery(ref('users_per_day_weekly_seasonal')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "volume_anomalies", "timestamp_column": "updated_at", "testing_timeframe": "1 day", "anomaly_threshold": 2}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_users_per_day_weekly_seasonal_14__day_of_week__2__updated_at.306327230f", "elementary_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_users_per_day_weekly_seasonal_14__day_of_week__2__updated_at.306327230f.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "users_per_day_weekly_seasonal", "column_name": null, "test_name": "volume_anomalies", "test_display_name": "Volume Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.users_per_day_weekly_seasonal", "table_unique_id": "elementary_tests.noya_tests.users_per_day_weekly_seasonal", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"timestamp_column": "updated_at", "sensitivity": 2, "backfill_days": 14, "seasonality": "day_of_week", "model": "{{ get_where_subquery(ref('users_per_day_weekly_seasonal')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "volume_anomalies", "timestamp_column": "updated_at", "testing_timeframe": "1 day", "anomaly_threshold": 2}}, "test_results": {"display_name": "Generic", "metrics": null, "result_description": null}}], "null": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.singular_test_with_no_ref", "elementary_unique_id": "test.elementary_integration_tests.singular_test_with_no_ref.None.singular", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": null, "column_name": null, "test_name": "singular_test_with_no_ref", "test_display_name": "Singular Test With No Ref", "latest_run_time": "2023-05-29T18:01:12+03:00", "latest_run_time_utc": "2023-05-29T15:01:12+00:00", "latest_run_status": "fail", "model_unique_id": null, "table_unique_id": "elementary_tests.noya_tests", "test_type": "dbt_test", "test_sub_type": "singular", "test_query": "select min from ELEMENTARY_TESTS.noya_tests.numeric_column_anomalies where min < 100", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "Got 96 results, configured to fail if != 0", "result_query": "select min from ELEMENTARY_TESTS.noya_tests.numeric_column_anomalies where min < 100"}, "configuration": {"test_name": "singular_test_with_no_ref", "test_params": {}}}, "test_results": {"display_name": "singular_test_with_no_ref", "results_sample": [{"min": 45.0}, {"min": 91.0}, {"min": 30.0}, {"min": 66.0}, {"min": 25.0}], "error_message": "Got 96 results, configured to fail if != 0", "failed_rows_count": 96}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.singular_test_with_two_refs", "elementary_unique_id": "test.elementary_integration_tests.singular_test_with_two_refs.None.singular", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": null, "column_name": null, "test_name": "singular_test_with_two_refs", "test_display_name": "Singular Test With Two Refs", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": null, "table_unique_id": "elementary_tests.noya_tests", "test_type": "dbt_test", "test_sub_type": "singular", "test_query": null, "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "singular_test_with_two_refs", "test_params": {}}}, "test_results": {"display_name": "singular_test_with_two_refs", "results_sample": null, "error_message": null, "failed_rows_count": -1}}], "source.elementary_integration_tests.training.numeric_column_anomalies_training": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.singular_test_with_source_ref", "elementary_unique_id": "test.elementary_integration_tests.singular_test_with_source_ref.None.singular", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "numeric_column_anomalies_training", "column_name": null, "test_name": "singular_test_with_source_ref", "test_display_name": "Singular Test With Source Ref", "latest_run_time": "2023-05-29T18:01:12+03:00", "latest_run_time_utc": "2023-05-29T15:01:12+00:00", "latest_run_status": "fail", "model_unique_id": "source.elementary_integration_tests.training.numeric_column_anomalies_training", "table_unique_id": "elementary_tests.test_seeds.numeric_column_anomalies_training", "test_type": "dbt_test", "test_sub_type": "singular", "test_query": "select min from ELEMENTARY_TESTS.test_seeds.numeric_column_anomalies_training where min < 105", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "Got 302 results, configured to fail if != 0", "result_query": "select min from ELEMENTARY_TESTS.test_seeds.numeric_column_anomalies_training where min < 105"}, "configuration": {"test_name": "singular_test_with_source_ref", "test_params": {}}}, "test_results": {"display_name": "singular_test_with_source_ref", "results_sample": [{"min": 104.0}, {"min": 101.0}, {"min": 101.0}, {"min": 102.0}, {"min": 102.0}], "error_message": "Got 302 results, configured to fail if != 0", "failed_rows_count": 302}}], "model.elementary_integration_tests.error_model": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.uniques_error_model_missing_column.3700cec9df", "elementary_unique_id": "test.elementary_integration_tests.uniques_error_model_missing_column.3700cec9df.missing_column.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "error_model", "column_name": "missing_column", "test_name": "uniques", "test_display_name": "Uniques", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.error_model", "table_unique_id": "elementary_tests.noya_tests.error_model", "test_type": "dbt_test", "test_sub_type": "generic", "test_query": null, "test_params": {"column_name": "missing_column", "model": "{{ get_where_subquery(ref('error_model')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "uniques", "test_params": {"column_name": "missing_column", "model": "{{ get_where_subquery(ref('error_model')) }}"}}}, "test_results": {"display_name": "uniques", "results_sample": null, "error_message": null, "failed_rows_count": -1}}]}, "test_results_totals": {"model.elementary_integration_tests.one": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "model.elementary_integration_tests.config_levels_project": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "model.elementary_integration_tests.config_levels_test_and_model": {"errors": 0, "warnings": 0, "passed": 2, "failures": 0}, "model.elementary_integration_tests.any_type_column_anomalies": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "model.elementary_integration_tests.copy_numeric_column_anomalies": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "model.elementary_integration_tests.ephemeral_model": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "model.elementary_integration_tests.numeric_column_anomalies": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "model.elementary_integration_tests.string_column_anomalies": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "model.elementary_integration_tests.backfill_days_column_anomalies": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "model.elementary_integration_tests.no_timestamp_anomalies": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "model.elementary_integration_tests.dimension_anomalies": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "model.elementary_integration_tests.groups": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "model.elementary_integration_tests.stats_players": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "model.elementary_integration_tests.stats_team": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "source.elementary_integration_tests.validation.any_type_column_anomalies_validation": {"errors": 0, "warnings": 0, "passed": 58, "failures": 1}, "source.elementary_integration_tests.training.any_type_column_anomalies_training": {"errors": 1, "warnings": 0, "passed": 2, "failures": 0}, "source.elementary_integration_tests.training.string_column_anomalies_training": {"errors": 0, "warnings": 0, "passed": 0, "failures": 3}, "model.elementary_integration_tests.users_per_day_weekly_seasonal": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "null": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "source.elementary_integration_tests.training.numeric_column_anomalies_training": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "model.elementary_integration_tests.error_model": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}}, "test_runs": {"model.elementary_integration_tests.one": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.accepted_values_one_one__2__3.5c148cffcc", "elementary_unique_id": "test.elementary_integration_tests.accepted_values_one_one__2__3.5c148cffcc.one.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "one", "column_name": "one", "test_name": "accepted_values", "test_display_name": "Accepted Values", "latest_run_time": "2023-05-30T20:02:44+03:00", "latest_run_time_utc": "2023-05-30T17:02:44+00:00", "latest_run_status": "fail", "model_unique_id": "model.elementary_integration_tests.one", "table_unique_id": "elementary_tests.noya_tests.one", "test_type": "dbt_test", "test_sub_type": "generic", "test_query": "with all_values as (\n\n select\n one as value_field,\n count(*) as n_records\n\n from ELEMENTARY_TESTS.noya_tests.one\n group by one\n\n)\n\nselect *\nfrom all_values\nwhere value_field not in (\n '2','3'\n)", "test_params": {"values": [2, 3], "column_name": "one", "model": "{{ get_where_subquery(ref('one')) }}"}, "test_created_at": null, "description": "This test validates that all of the values in a column are present in a supplied list of `values`. If any values other than those provided in the list are present, then the test will fail.", "result": {"result_description": "Got 1 result, configured to fail if != 0", "result_query": "with all_values as (\n\n select\n one as value_field,\n count(*) as n_records\n\n from ELEMENTARY_TESTS.noya_tests.one\n group by one\n\n)\n\nselect *\nfrom all_values\nwhere value_field not in (\n '2','3'\n)"}, "configuration": {"test_name": "accepted_values", "test_params": {"values": [2, 3], "column_name": "one", "model": "{{ get_where_subquery(ref('one')) }}"}}}, "test_runs": {"fail_rate": 1.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 7}, "invocations": [{"affected_rows": 1, "time_utc": "2023-05-29T14:43:22+00:00", "id": "9fe9080e-71c1-4115-ad97-efa22e3ef0ba", "status": "fail"}, {"affected_rows": 1, "time_utc": "2023-05-29T14:46:27+00:00", "id": "a6955aa8-fb4f-412a-b5ca-3971bd9e316f", "status": "fail"}, {"affected_rows": 1, "time_utc": "2023-05-29T14:49:52+00:00", "id": "bdc910ec-e261-4c2b-b5e9-d7a0dce600fb", "status": "fail"}, {"affected_rows": 1, "time_utc": "2023-05-29T14:50:43+00:00", "id": "230dcd5f-a187-4225-9436-86ad10b82fa3", "status": "fail"}, {"affected_rows": 1, "time_utc": "2023-05-29T14:51:39+00:00", "id": "50a16eb2-620e-4591-b4d0-df99ac5f8e74", "status": "fail"}, {"affected_rows": 1, "time_utc": "2023-05-29T15:01:12+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "fail"}, {"affected_rows": 1, "time_utc": "2023-05-30T17:02:44+00:00", "id": "63973590-b12a-436c-8b2b-62f306e31de9", "status": "fail"}], "description": "There were 7 failures, no errors and no warnings on the last 7 test runs."}}], "model.elementary_integration_tests.config_levels_project": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.config_levels_config_levels_project__period_day_count_1_.e491a0d999", "elementary_unique_id": "test.elementary_integration_tests.config_levels_config_levels_project__period_day_count_1_.e491a0d999.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "config_levels_project", "column_name": null, "test_name": "config_levels", "test_display_name": "Config Levels", "latest_run_time": "2023-05-29T18:01:12+03:00", "latest_run_time_utc": "2023-05-29T15:01:12+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.config_levels_project", "table_unique_id": "elementary_tests.noya_tests.config_levels_project", "test_type": "dbt_test", "test_sub_type": "generic", "test_query": "with nothing as (select 1 as num)\n select * from nothing where num = 2", "test_params": {"expected_config": {"time_bucket": {"period": "day", "count": 1}}, "model": "{{ get_where_subquery(ref('config_levels_project')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": "with nothing as (select 1 as num)\n select * from nothing where num = 2"}, "configuration": {"test_name": "config_levels", "test_params": {"expected_config": {"time_bucket": {"period": "day", "count": 1}}, "model": "{{ get_where_subquery(ref('config_levels_project')) }}"}}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:12+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}], "model.elementary_integration_tests.config_levels_test_and_model": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.config_levels_config_levels_test_and_model__period_day_count_3_.462f85ebd5", "elementary_unique_id": "test.elementary_integration_tests.config_levels_config_levels_test_and_model__period_day_count_3_.462f85ebd5.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "config_levels_test_and_model", "column_name": null, "test_name": "config_levels", "test_display_name": "Config Levels", "latest_run_time": "2023-05-29T18:01:12+03:00", "latest_run_time_utc": "2023-05-29T15:01:12+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.config_levels_test_and_model", "table_unique_id": "elementary_tests.noya_tests.config_levels_test_and_model", "test_type": "dbt_test", "test_sub_type": "generic", "test_query": "with nothing as (select 1 as num)\n select * from nothing where num = 2", "test_params": {"expected_config": {"time_bucket": {"period": "day", "count": 3}}, "model": "{{ get_where_subquery(ref('config_levels_test_and_model')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": "with nothing as (select 1 as num)\n select * from nothing where num = 2"}, "configuration": {"test_name": "config_levels", "test_params": {"expected_config": {"time_bucket": {"period": "day", "count": 3}}, "model": "{{ get_where_subquery(ref('config_levels_test_and_model')) }}"}}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:12+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.config_levels_config_levels_test_and_model__period_hour_count_4___hour__4.4745d4716f", "elementary_unique_id": "test.elementary_integration_tests.config_levels_config_levels_test_and_model__period_hour_count_4___hour__4.4745d4716f.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "config_levels_test_and_model", "column_name": null, "test_name": "config_levels", "test_display_name": "Config Levels", "latest_run_time": "2023-05-29T18:01:12+03:00", "latest_run_time_utc": "2023-05-29T15:01:12+00:00", "latest_run_status": "pass", "model_unique_id": "model.elementary_integration_tests.config_levels_test_and_model", "table_unique_id": "elementary_tests.noya_tests.config_levels_test_and_model", "test_type": "dbt_test", "test_sub_type": "generic", "test_query": "with nothing as (select 1 as num)\n select * from nothing where num = 2", "test_params": {"time_bucket": {"period": "hour", "count": 4}, "expected_config": {"time_bucket": {"period": "hour", "count": 4}}, "model": "{{ get_where_subquery(ref('config_levels_test_and_model')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": "with nothing as (select 1 as num)\n select * from nothing where num = 2"}, "configuration": {"test_name": "config_levels", "test_params": {"time_bucket": {"period": "hour", "count": 4}, "expected_config": {"time_bucket": {"period": "hour", "count": 4}}, "model": "{{ get_where_subquery(ref('config_levels_test_and_model')) }}"}}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:12+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}], "model.elementary_integration_tests.any_type_column_anomalies": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c", "elementary_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "any_type_column_anomalies", "column_name": null, "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"exclude_regexp": ".*column1|column2|column3|column4|column5|column6|column7|column8|column9|column10|column11|column12|column13|column14|column15|column16|column17.*", "model": "{{ get_where_subquery(ref('any_type_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies_drop.3e202bb243", "elementary_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies_drop.3e202bb243.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "any_type_column_anomalies", "column_name": null, "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"anomaly_direction": "drop", "model": "{{ get_where_subquery(ref('any_type_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies_spike.1f1909a57c", "elementary_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies_spike.1f1909a57c.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "any_type_column_anomalies", "column_name": null, "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"anomaly_direction": "spike", "model": "{{ get_where_subquery(ref('any_type_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5", "elementary_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "any_type_column_anomalies", "column_name": null, "test_name": "volume_anomalies", "test_display_name": "Volume Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"time_bucket": {"period": "hour", "count": 4}, "model": "{{ get_where_subquery(ref('any_type_column_anomalies')) }}"}, "test_created_at": null, "description": "This is a very weird description with breaklines and comma, and even a string like this 'wow'. You know, these $##$34#@#!^ can also be helpful WDYT?\n", "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "volume_anomalies", "timestamp_column": null, "testing_timeframe": "4 hours", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_week__1.ef6d0eff06", "elementary_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_week__1.ef6d0eff06.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "any_type_column_anomalies", "column_name": null, "test_name": "volume_anomalies", "test_display_name": "Volume Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.any_type_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"time_bucket": {"period": "week", "count": 1}, "model": "{{ get_where_subquery(ref('any_type_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "volume_anomalies", "timestamp_column": null, "testing_timeframe": "1 week", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.generic_test_on_model_any_type_column_anomalies_.a9e77d8087", "elementary_unique_id": "test.elementary_integration_tests.generic_test_on_model_any_type_column_anomalies_.a9e77d8087.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "any_type_column_anomalies", "column_name": null, "test_name": "generic_test_on_model", "test_display_name": "Generic Test On Model", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.any_type_column_anomalies", "test_type": "dbt_test", "test_sub_type": "generic", "test_query": null, "test_params": {"model": "{{ get_where_subquery(ref('any_type_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "generic_test_on_model", "test_params": {"model": "{{ get_where_subquery(ref('any_type_column_anomalies')) }}"}}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}], "model.elementary_integration_tests.copy_numeric_column_anomalies": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148", "elementary_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "copy_numeric_column_anomalies", "column_name": null, "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.copy_numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.copy_numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_anomalies": ["zero_count"], "model": "{{ get_where_subquery(ref('copy_numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}], "model.elementary_integration_tests.ephemeral_model": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_ephemeral_model_.69d9c5e486", "elementary_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_ephemeral_model_.69d9c5e486.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "ephemeral_model", "column_name": null, "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.ephemeral_model", "table_unique_id": "elementary_tests.noya_tests.ephemeral_model", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"model": "{{ get_where_subquery(ref('ephemeral_model')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_ephemeral_model_.085bb21469", "elementary_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_ephemeral_model_.085bb21469.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "ephemeral_model", "column_name": null, "test_name": "freshness_anomalies", "test_display_name": "Freshness Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.ephemeral_model", "table_unique_id": "elementary_tests.noya_tests.ephemeral_model", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"model": "{{ get_where_subquery(ref('ephemeral_model')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "freshness_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_schema_changes_ephemeral_model_.4470433d4c", "elementary_unique_id": "test.elementary_integration_tests.elementary_schema_changes_ephemeral_model_.4470433d4c", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "ephemeral_model", "column_name": null, "test_name": "schema_changes", "test_display_name": "Schema Changes", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.ephemeral_model", "table_unique_id": "elementary_tests.noya_tests.ephemeral_model", "test_type": "schema_change", "test_sub_type": "generic", "test_query": null, "test_params": {"model": "{{ get_where_subquery(ref('ephemeral_model')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "schema_changes", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_ephemeral_model_.4b08aa00b7", "elementary_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_ephemeral_model_.4b08aa00b7.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "ephemeral_model", "column_name": null, "test_name": "volume_anomalies", "test_display_name": "Volume Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.ephemeral_model", "table_unique_id": "elementary_tests.noya_tests.ephemeral_model", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"model": "{{ get_where_subquery(ref('ephemeral_model')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "volume_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}], "model.elementary_integration_tests.numeric_column_anomalies": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_numeric_column_anomalies_average_length__null_count.4719a95b87", "elementary_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_numeric_column_anomalies_average_length__null_count.4719a95b87.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": null, "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_anomalies": ["average_length", "null_count"], "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde.average.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": "average", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_anomalies": ["average"], "column_name": "average", "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb.max.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": "max", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_anomalies": ["average"], "column_name": "max", "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7.min.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": "min", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_anomalies": ["average"], "column_name": "min", "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_drop__average__max.e87fc4578f", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_drop__average__max.e87fc4578f.max.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": "max", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_anomalies": ["average"], "anomaly_direction": "drop", "column_name": "max", "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba.average.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": "average", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_anomalies": ["max"], "column_name": "average", "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__max.21a73d9fec", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__max.21a73d9fec.max.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": "max", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_anomalies": ["max"], "column_name": "max", "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__min.6758a0b107", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__min.6758a0b107.min.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": "min", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_anomalies": ["max"], "column_name": "min", "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e.average.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": "average", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_anomalies": ["min"], "column_name": "average", "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__max.9841e551cc", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__max.9841e551cc.max.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": "max", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_anomalies": ["min"], "column_name": "max", "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__min.72357fb8ab", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__min.72357fb8ab.min.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": "min", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_anomalies": ["min"], "column_name": "min", "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_spike__average__max.d74f23e2ef", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_spike__average__max.d74f23e2ef.max.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": "max", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_anomalies": ["average"], "anomaly_direction": "spike", "column_name": "max", "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84.standard_deviation.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": "standard_deviation", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_name": "standard_deviation", "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_sum__sum.6ede4629eb", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_sum__sum.6ede4629eb.sum.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": "sum", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_anomalies": ["sum"], "column_name": "sum", "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_updated_at.b0d22411ff", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_updated_at.b0d22411ff.updated_at.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": "updated_at", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_name": "updated_at", "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_variance.ccbeab9e37", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_variance.ccbeab9e37.variance.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": "variance", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_name": "variance", "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_count.35e5387f41", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_count.35e5387f41.zero_count.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": "zero_count", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_name": "zero_count", "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8.zero_percent.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": "zero_percent", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_name": "zero_percent", "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_event_freshness_anomalies_numeric_column_anomalies_occurred_at__updated_at.1ca7b9299b", "elementary_unique_id": "test.elementary_integration_tests.elementary_event_freshness_anomalies_numeric_column_anomalies_occurred_at__updated_at.1ca7b9299b.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": null, "test_name": "event_freshness_anomalies", "test_display_name": "Event Freshness Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"event_timestamp_column": "occurred_at", "update_timestamp_column": "updated_at", "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "event_freshness_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c", "elementary_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": null, "test_name": "freshness_anomalies", "test_display_name": "Freshness Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "freshness_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_schema_changes_numeric_column_anomalies_.1d9f82cc93", "elementary_unique_id": "test.elementary_integration_tests.elementary_schema_changes_numeric_column_anomalies_.1d9f82cc93", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": null, "test_name": "schema_changes", "test_display_name": "Schema Changes", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "schema_change", "test_sub_type": "generic", "test_query": null, "test_params": {"model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "schema_changes", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38", "elementary_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": null, "test_name": "volume_anomalies", "test_display_name": "Volume Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "volume_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_drop.c2dea8af95", "elementary_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_drop.c2dea8af95.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": null, "test_name": "volume_anomalies", "test_display_name": "Volume Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"anomaly_direction": "drop", "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "volume_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_spike.d99efa2f8a", "elementary_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_spike.d99efa2f8a.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": null, "test_name": "volume_anomalies", "test_display_name": "Volume Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"anomaly_direction": "spike", "model": "{{ get_where_subquery(ref('numeric_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "volume_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.singular_test_with_one_ref", "elementary_unique_id": "test.elementary_integration_tests.singular_test_with_one_ref.None.singular", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "numeric_column_anomalies", "column_name": null, "test_name": "singular_test_with_one_ref", "test_display_name": "Singular Test With One Ref", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.numeric_column_anomalies", "test_type": "dbt_test", "test_sub_type": "singular", "test_query": null, "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "singular_test_with_one_ref", "test_params": {}}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}], "model.elementary_integration_tests.string_column_anomalies": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_string_column_anomalies_.151e5a09d5", "elementary_unique_id": "test.elementary_integration_tests.elementary_all_columns_anomalies_string_column_anomalies_.151e5a09d5.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "string_column_anomalies", "column_name": null, "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"model": "{{ get_where_subquery(ref('string_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2.average_length.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "string_column_anomalies", "column_name": "average_length", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_anomalies": ["average_length", "null_count"], "column_name": "average_length", "model": "{{ get_where_subquery(ref('string_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_max_length.5c7beb9c06", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_max_length.5c7beb9c06.max_length.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "string_column_anomalies", "column_name": "max_length", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_name": "max_length", "model": "{{ get_where_subquery(ref('string_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8.min_length.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "string_column_anomalies", "column_name": "min_length", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_anomalies": ["min_length", "max_length", "missing_count"], "column_name": "min_length", "model": "{{ get_where_subquery(ref('string_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d.missing_count.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "string_column_anomalies", "column_name": "missing_count", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_name": "missing_count", "model": "{{ get_where_subquery(ref('string_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771.missing_percent.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "string_column_anomalies", "column_name": "missing_percent", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_name": "missing_percent", "model": "{{ get_where_subquery(ref('string_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_updated_at.8901e974a6", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_updated_at.8901e974a6.updated_at.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "string_column_anomalies", "column_name": "updated_at", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_name": "updated_at", "model": "{{ get_where_subquery(ref('string_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_event_freshness_anomalies_string_column_anomalies_occurred_at__updated_at.e4db4306c6", "elementary_unique_id": "test.elementary_integration_tests.elementary_event_freshness_anomalies_string_column_anomalies_occurred_at__updated_at.e4db4306c6.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "string_column_anomalies", "column_name": null, "test_name": "event_freshness_anomalies", "test_display_name": "Event Freshness Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"event_timestamp_column": "occurred_at", "update_timestamp_column": "updated_at", "model": "{{ get_where_subquery(ref('string_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "event_freshness_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93", "elementary_unique_id": "test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "string_column_anomalies", "column_name": null, "test_name": "freshness_anomalies", "test_display_name": "Freshness Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.string_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"model": "{{ get_where_subquery(ref('string_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "freshness_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_schema_changes_string_column_anomalies_.c57cd75b87", "elementary_unique_id": "test.elementary_integration_tests.elementary_schema_changes_string_column_anomalies_.c57cd75b87", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "string_column_anomalies", "column_name": null, "test_name": "schema_changes", "test_display_name": "Schema Changes", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.string_column_anomalies", "test_type": "schema_change", "test_sub_type": "generic", "test_query": null, "test_params": {"model": "{{ get_where_subquery(ref('string_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "schema_changes", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.relationships_string_column_anomalies_min_length__max_length__source_training_string_column_anomalies_training_.a2cbc353c6", "elementary_unique_id": "test.elementary_integration_tests.relationships_string_column_anomalies_min_length__max_length__source_training_string_column_anomalies_training_.a2cbc353c6.min_length.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "string_column_anomalies", "column_name": "min_length", "test_name": "relationships", "test_display_name": "Relationships", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.string_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.string_column_anomalies", "test_type": "dbt_test", "test_sub_type": "generic", "test_query": null, "test_params": {"to": "source('training', 'string_column_anomalies_training')", "field": "max_length", "column_name": "min_length", "model": "{{ get_where_subquery(ref('string_column_anomalies')) }}"}, "test_created_at": null, "description": "This test validates that all of the records in a child table have a corresponding record in a parent table. This property is referred to as \"referential integrity\".", "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "relationships", "test_params": {"to": "source('training', 'string_column_anomalies_training')", "field": "max_length", "column_name": "min_length", "model": "{{ get_where_subquery(ref('string_column_anomalies')) }}"}}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}], "model.elementary_integration_tests.backfill_days_column_anomalies": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_backfill_days_column_anomalies_7__min_length__max_length__min_length.437faf5372", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_backfill_days_column_anomalies_7__min_length__max_length__min_length.437faf5372.min_length.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "backfill_days_column_anomalies", "column_name": "min_length", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.backfill_days_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.backfill_days_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"backfill_days": 7, "column_anomalies": ["min_length", "max_length"], "column_name": "min_length", "model": "{{ get_where_subquery(ref('backfill_days_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_backfill_days_column_anomalies_min_length__max_length__min_length.9cf2f5f6ad", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_backfill_days_column_anomalies_min_length__max_length__min_length.9cf2f5f6ad.min_length.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "backfill_days_column_anomalies", "column_name": "min_length", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.backfill_days_column_anomalies", "table_unique_id": "elementary_tests.noya_tests.backfill_days_column_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_anomalies": ["min_length", "max_length"], "column_name": "min_length", "model": "{{ get_where_subquery(ref('backfill_days_column_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}], "model.elementary_integration_tests.no_timestamp_anomalies": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_no_timestamp_anomalies_null_count__null_count_str.6532606df5", "elementary_unique_id": "test.elementary_integration_tests.elementary_column_anomalies_no_timestamp_anomalies_null_count__null_count_str.6532606df5.null_count_str.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "no_timestamp_anomalies", "column_name": "null_count_str", "test_name": "column_anomalies", "test_display_name": "Column Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.no_timestamp_anomalies", "table_unique_id": "elementary_tests.noya_tests.no_timestamp_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"column_anomalies": ["null_count"], "column_name": "null_count_str", "model": "{{ get_where_subquery(ref('no_timestamp_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "column_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_dimension_anomalies_no_timestamp_anomalies_null_count_str.cf20940f2d", "elementary_unique_id": "test.elementary_integration_tests.elementary_dimension_anomalies_no_timestamp_anomalies_null_count_str.cf20940f2d", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "no_timestamp_anomalies", "column_name": null, "test_name": "dimension_anomalies", "test_display_name": "Dimension Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.no_timestamp_anomalies", "table_unique_id": "elementary_tests.noya_tests.no_timestamp_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"dimensions": ["null_count_str"], "model": "{{ get_where_subquery(ref('no_timestamp_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "dimension_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_no_timestamp_anomalies_.73ede8a6cc", "elementary_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_no_timestamp_anomalies_.73ede8a6cc.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "no_timestamp_anomalies", "column_name": null, "test_name": "volume_anomalies", "test_display_name": "Volume Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.no_timestamp_anomalies", "table_unique_id": "elementary_tests.noya_tests.no_timestamp_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"model": "{{ get_where_subquery(ref('no_timestamp_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "volume_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}], "model.elementary_integration_tests.dimension_anomalies": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_dimension_anomalies_dimension_anomalies_drop__platform.021a36a88a", "elementary_unique_id": "test.elementary_integration_tests.elementary_dimension_anomalies_dimension_anomalies_drop__platform.021a36a88a", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "dimension_anomalies", "column_name": null, "test_name": "dimension_anomalies", "test_display_name": "Dimension Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.dimension_anomalies", "table_unique_id": "elementary_tests.noya_tests.dimension_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"anomaly_direction": "drop", "dimensions": ["platform"], "model": "{{ get_where_subquery(ref('dimension_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "dimension_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_dimension_anomalies_dimension_anomalies_platform.cf343e4b29", "elementary_unique_id": "test.elementary_integration_tests.elementary_dimension_anomalies_dimension_anomalies_platform.cf343e4b29", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "dimension_anomalies", "column_name": null, "test_name": "dimension_anomalies", "test_display_name": "Dimension Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.dimension_anomalies", "table_unique_id": "elementary_tests.noya_tests.dimension_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"dimensions": ["platform"], "model": "{{ get_where_subquery(ref('dimension_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "dimension_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_dimension_anomalies_dimension_anomalies_platform__platform_android_.89f503656a", "elementary_unique_id": "test.elementary_integration_tests.elementary_dimension_anomalies_dimension_anomalies_platform__platform_android_.89f503656a", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "dimension_anomalies", "column_name": null, "test_name": "dimension_anomalies", "test_display_name": "Dimension Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.dimension_anomalies", "table_unique_id": "elementary_tests.noya_tests.dimension_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"dimensions": ["platform"], "where_expression": "platform = 'android'", "model": "{{ get_where_subquery(ref('dimension_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "dimension_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_dimension_anomalies_dimension_anomalies_platform__version.b65d46fbf9", "elementary_unique_id": "test.elementary_integration_tests.elementary_dimension_anomalies_dimension_anomalies_platform__version.b65d46fbf9", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "dimension_anomalies", "column_name": null, "test_name": "dimension_anomalies", "test_display_name": "Dimension Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.dimension_anomalies", "table_unique_id": "elementary_tests.noya_tests.dimension_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"dimensions": ["platform", "version"], "model": "{{ get_where_subquery(ref('dimension_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "dimension_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_dimension_anomalies_dimension_anomalies_spike__platform.ae9aad0d02", "elementary_unique_id": "test.elementary_integration_tests.elementary_dimension_anomalies_dimension_anomalies_spike__platform.ae9aad0d02", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "dimension_anomalies", "column_name": null, "test_name": "dimension_anomalies", "test_display_name": "Dimension Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.dimension_anomalies", "table_unique_id": "elementary_tests.noya_tests.dimension_anomalies", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"anomaly_direction": "spike", "dimensions": ["platform"], "model": "{{ get_where_subquery(ref('dimension_anomalies')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "dimension_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}], "model.elementary_integration_tests.groups": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_schema_changes_from_baseline_groups_True.7468e2e161", "elementary_unique_id": "test.elementary_integration_tests.elementary_schema_changes_from_baseline_groups_True.7468e2e161", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "groups", "column_name": null, "test_name": "schema_changes_from_baseline", "test_display_name": "Schema Changes From Baseline", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.groups", "table_unique_id": "elementary_tests.noya_tests.groups", "test_type": "schema_change", "test_sub_type": "generic", "test_query": null, "test_params": {"enforce_types": true, "model": "{{ get_where_subquery(ref('groups')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "schema_changes_from_baseline", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_schema_changes_from_baseline_groups_True.973df95f83", "elementary_unique_id": "test.elementary_integration_tests.elementary_schema_changes_from_baseline_groups_True.973df95f83", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "groups", "column_name": null, "test_name": "schema_changes_from_baseline", "test_display_name": "Schema Changes From Baseline", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.groups", "table_unique_id": "elementary_tests.noya_tests.groups", "test_type": "schema_change", "test_sub_type": "generic", "test_query": null, "test_params": {"fail_on_added": true, "model": "{{ get_where_subquery(ref('groups')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "schema_changes_from_baseline", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_schema_changes_groups_.b321d3c7ba", "elementary_unique_id": "test.elementary_integration_tests.elementary_schema_changes_groups_.b321d3c7ba", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "groups", "column_name": null, "test_name": "schema_changes", "test_display_name": "Schema Changes", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.groups", "table_unique_id": "elementary_tests.noya_tests.groups", "test_type": "schema_change", "test_sub_type": "generic", "test_query": null, "test_params": {"model": "{{ get_where_subquery(ref('groups')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "schema_changes", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}], "model.elementary_integration_tests.stats_players": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_schema_changes_from_baseline_stats_players_.02157887f9", "elementary_unique_id": "test.elementary_integration_tests.elementary_schema_changes_from_baseline_stats_players_.02157887f9", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "stats_players", "column_name": null, "test_name": "schema_changes_from_baseline", "test_display_name": "Schema Changes From Baseline", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.stats_players", "table_unique_id": "elementary_tests.noya_tests.stats_players", "test_type": "schema_change", "test_sub_type": "generic", "test_query": null, "test_params": {"model": "{{ get_where_subquery(ref('stats_players')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "schema_changes_from_baseline", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_schema_changes_from_baseline_stats_players_True.1447622942", "elementary_unique_id": "test.elementary_integration_tests.elementary_schema_changes_from_baseline_stats_players_True.1447622942", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "stats_players", "column_name": null, "test_name": "schema_changes_from_baseline", "test_display_name": "Schema Changes From Baseline", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.stats_players", "table_unique_id": "elementary_tests.noya_tests.stats_players", "test_type": "schema_change", "test_sub_type": "generic", "test_query": null, "test_params": {"enforce_types": true, "model": "{{ get_where_subquery(ref('stats_players')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "schema_changes_from_baseline", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_schema_changes_stats_players_.388c33d77b", "elementary_unique_id": "test.elementary_integration_tests.elementary_schema_changes_stats_players_.388c33d77b", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "stats_players", "column_name": null, "test_name": "schema_changes", "test_display_name": "Schema Changes", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.stats_players", "table_unique_id": "elementary_tests.noya_tests.stats_players", "test_type": "schema_change", "test_sub_type": "generic", "test_query": null, "test_params": {"model": "{{ get_where_subquery(ref('stats_players')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "schema_changes", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}], "model.elementary_integration_tests.stats_team": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_schema_changes_stats_team_.fe2147cc27", "elementary_unique_id": "test.elementary_integration_tests.elementary_schema_changes_stats_team_.fe2147cc27", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "stats_team", "column_name": null, "test_name": "schema_changes", "test_display_name": "Schema Changes", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.stats_team", "table_unique_id": "elementary_tests.noya_tests.stats_team", "test_type": "schema_change", "test_sub_type": "generic", "test_query": null, "test_params": {"model": "{{ get_where_subquery(ref('stats_team')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "schema_changes", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}], "source.elementary_integration_tests.validation.any_type_column_anomalies_validation": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_BOOL.null_count", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_BOOL", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "null_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_BOOL' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_BOOL' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_BOOL.null_percent", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_BOOL", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "null_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_BOOL' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_BOOL' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_FLOAT.average", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "average", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_FLOAT.max", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "max", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('max' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('max' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_FLOAT.min", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "min", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('min' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('min' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_FLOAT.null_count", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "null_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_FLOAT.null_percent", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "null_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_FLOAT.standard_deviation", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "standard_deviation", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('standard_deviation' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('standard_deviation' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_FLOAT.variance", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "variance", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('variance' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('variance' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_FLOAT.zero_count", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "zero_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('zero_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('zero_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_FLOAT.zero_percent", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "zero_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('zero_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('zero_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_INT.average", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "average", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_INT.max", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "max", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('max' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('max' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_INT.min", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "min", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('min' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('min' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_INT.null_count", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "null_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_INT.null_percent", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "null_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_INT.standard_deviation", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "standard_deviation", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('standard_deviation' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('standard_deviation' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_INT.variance", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "variance", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('variance' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('variance' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_INT.zero_count", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "zero_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('zero_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('zero_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_INT.zero_percent", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "zero_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('zero_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('zero_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_STR.average_length", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "average_length", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('average_length' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('average_length' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_STR.max_length", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "max_length", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('max_length' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('max_length' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_STR.min_length", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "min_length", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('min_length' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('min_length' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_STR.missing_count", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "missing_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('missing_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('missing_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_STR.missing_percent", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "missing_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('missing_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('missing_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_STR.null_count", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "null_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_COUNT_STR.null_percent", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_COUNT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "null_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_COUNT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_BOOL.null_count", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_BOOL", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "null_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_BOOL' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_BOOL' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_BOOL.null_percent", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_BOOL", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "null_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_BOOL' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_BOOL' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_FLOAT.average", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "average", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_FLOAT.max", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "max", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('max' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('max' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_FLOAT.min", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "min", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('min' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('min' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_FLOAT.null_count", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "null_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_FLOAT.null_percent", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "null_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_FLOAT.standard_deviation", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "standard_deviation", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('standard_deviation' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('standard_deviation' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_FLOAT.variance", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "variance", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('variance' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('variance' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_FLOAT.zero_count", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "zero_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('zero_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('zero_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_FLOAT.zero_percent", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_FLOAT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "zero_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('zero_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('zero_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_FLOAT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_INT.average", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "average", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('average' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_INT.max", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "max", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('max' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('max' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_INT.min", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "min", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('min' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('min' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_INT.null_count", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "null_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_INT.null_percent", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "null_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_INT.standard_deviation", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "standard_deviation", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('standard_deviation' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('standard_deviation' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_INT.variance", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "variance", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('variance' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('variance' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_INT.zero_count", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "zero_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('zero_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('zero_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_INT.zero_percent", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_INT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "zero_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('zero_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('zero_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_INT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_STR.average_length", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "average_length", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('average_length' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('average_length' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_STR.max_length", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "max_length", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('max_length' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('max_length' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_STR.min_length", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "min_length", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('min_length' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('min_length' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_STR.missing_count", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "missing_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('missing_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('missing_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_STR.missing_percent", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "missing_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('missing_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('missing_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_STR.null_count", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "null_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.NULL_PERCENT_STR.null_percent", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "NULL_PERCENT_STR", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "null_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('NULL_PERCENT_STR' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.OCCURRED_AT.null_count", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "OCCURRED_AT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "null_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('OCCURRED_AT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('OCCURRED_AT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.OCCURRED_AT.null_percent", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "OCCURRED_AT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "null_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('OCCURRED_AT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('OCCURRED_AT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.UPDATED_AT.null_count", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "UPDATED_AT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "null_count", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('UPDATED_AT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_count' as varchar)\n and upper(column_name) = upper(cast('UPDATED_AT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67.UPDATED_AT.null_percent", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "UPDATED_AT", "test_name": "all_columns_anomalies", "test_display_name": "All Columns Anomalies", "latest_run_time": "2023-05-29T18:01:27+03:00", "latest_run_time_utc": "2023-05-29T15:01:27+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "anomaly_detection", "test_sub_type": "null_percent", "test_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('UPDATED_AT' as varchar))", "test_params": {"model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n \n \n\n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n\n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n \n \n\n \n \n\n \n\n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_0A44BF5C67_ELEMENTARY_SOURCE_ALL_COLUMNS_ANOMALIES_VALIDATION_ANY_TYPE_COLUMN_ANOMALIES_VALIDATION___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_VALIDATION' as varchar)) and\n metric_name = cast('null_percent' as varchar)\n and upper(column_name) = upper(cast('UPDATED_AT' as varchar))"}, "configuration": {"test_name": "all_columns_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:27+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.source_generic_test_on_column_validation_any_type_column_anomalies_validation_null_count_int.13c361724d", "elementary_unique_id": "test.elementary_integration_tests.source_generic_test_on_column_validation_any_type_column_anomalies_validation_null_count_int.13c361724d.null_count_int.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_validation", "column_name": "null_count_int", "test_name": "generic_test_on_column", "test_display_name": "Generic Test On Column", "latest_run_time": "2023-05-29T18:01:12+03:00", "latest_run_time_utc": "2023-05-29T15:01:12+00:00", "latest_run_status": "fail", "model_unique_id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_validation", "test_type": "dbt_test", "test_sub_type": "generic", "test_query": "with nothing as (select 1 as num)\n select * from nothing where num = 1", "test_params": {"column_name": "null_count_int", "model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": "Got 1 result, configured to fail if != 0", "result_query": "with nothing as (select 1 as num)\n select * from nothing where num = 1"}, "configuration": {"test_name": "generic_test_on_column", "test_params": {"column_name": "null_count_int", "model": "{{ get_where_subquery(source('validation', 'any_type_column_anomalies_validation')) }}"}}}, "test_runs": {"fail_rate": 1.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": 1, "time_utc": "2023-05-29T15:01:12+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}], "source.elementary_integration_tests.training.any_type_column_anomalies_training": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_any_type_column_anomalies_training_occurred_at.50c4b3e11b", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_any_type_column_anomalies_training_occurred_at.50c4b3e11b.None.event_freshness", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_training", "column_name": null, "test_name": "event_freshness_anomalies", "test_display_name": "Event Freshness Anomalies", "latest_run_time": "2023-05-29T18:01:20+03:00", "latest_run_time_utc": "2023-05-29T15:01:20+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.training.any_type_column_anomalies_training", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_training", "test_type": "anomaly_detection", "test_sub_type": "event_freshness", "test_query": "select * from (\n \n\n \n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n\n \n \n \n \n\n \n \n\n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n \n\n \n\n \n \n \n\n\n \n \n \n\n \n \n \n\n \n \n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_50C4B3E11B_ELEMENTARY_SOURCE_EVENT_FRESHNESS_ANOMALIES_TRAINING_ANY_TYPE_COLUMN_ANOMALIES_TRAINING_OCCURRED_AT__ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n\n \n\n\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_TRAINING' as varchar)) and\n metric_name = cast('event_freshness' as varchar)", "test_params": {"event_timestamp_column": "occurred_at", "model": "{{ get_where_subquery(source('training', 'any_type_column_anomalies_training')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n \n\n \n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n\n \n \n \n \n\n \n \n\n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n \n\n \n\n \n \n \n\n\n \n \n \n\n \n \n \n\n \n \n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_50C4B3E11B_ELEMENTARY_SOURCE_EVENT_FRESHNESS_ANOMALIES_TRAINING_ANY_TYPE_COLUMN_ANOMALIES_TRAINING_OCCURRED_AT__ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n\n \n\n\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_TRAINING' as varchar)) and\n metric_name = cast('event_freshness' as varchar)"}, "configuration": {"test_name": "event_freshness_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:20+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_any_type_column_anomalies_training_.5733415eda", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_any_type_column_anomalies_training_.5733415eda.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_training", "column_name": null, "test_name": "freshness_anomalies", "test_display_name": "Freshness Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "error", "model_unique_id": "source.elementary_integration_tests.training.any_type_column_anomalies_training", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_training", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"model": "{{ get_where_subquery(source('training', 'any_type_column_anomalies_training')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": "Compilation Error in test elementary_source_freshness_anomalies_training_any_type_column_anomalies_training_ (models/schema.yml)\n freshness_anomalies test is not supported without timestamp_column.\n \n > in macro freshness_metric_query (macros/edr/data_monitoring/monitors_query/table_monitoring_query.sql)\n > called by macro get_metric_query (macros/edr/data_monitoring/monitors_query/table_monitoring_query.sql)\n > called by macro get_unified_metrics_query (macros/edr/data_monitoring/monitors_query/table_monitoring_query.sql)\n > called by macro table_monitoring_query (macros/edr/data_monitoring/monitors_query/table_monitoring_query.sql)\n > called by macro test_table_anomalies (macros/edr/tests/test_table_anomalies.sql)\n > called by macro test_freshness_anomalies (macros/edr/tests/test_freshness_anomalies.sql)\n > called by test elementary_source_freshness_anomalies_training_any_type_column_anomalies_training_ (models/schema.yml)", "result_query": null}, "configuration": {"test_name": "freshness_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": null}}, "test_runs": {"fail_rate": 1.0, "totals": {"errors": 1, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "error"}], "description": "There were no failures, 1 errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_any_type_column_anomalies_training_.e6ec3c50e5", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_any_type_column_anomalies_training_.e6ec3c50e5.None.row_count", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "any_type_column_anomalies_training", "column_name": null, "test_name": "volume_anomalies", "test_display_name": "Volume Anomalies", "latest_run_time": "2023-05-29T18:01:20+03:00", "latest_run_time_utc": "2023-05-29T15:01:20+00:00", "latest_run_status": "pass", "model_unique_id": "source.elementary_integration_tests.training.any_type_column_anomalies_training", "table_unique_id": "elementary_tests.test_seeds.any_type_column_anomalies_training", "test_type": "anomaly_detection", "test_sub_type": "row_count", "test_query": "select * from (\n \n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n\n \n \n \n \n\n \n \n\n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n \n\n \n\n \n \n \n\n\n \n \n \n\n \n \n \n\n \n \n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_E6EC3C50E5_ELEMENTARY_SOURCE_VOLUME_ANOMALIES_TRAINING_ANY_TYPE_COLUMN_ANOMALIES_TRAINING___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n\n \n\n\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_TRAINING' as varchar)) and\n metric_name = cast('row_count' as varchar)", "test_params": {"model": "{{ get_where_subquery(source('training', 'any_type_column_anomalies_training')) }}", "sensitivity": 3, "timestamp_column": null, "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Not enough data to calculate anomaly score.", "result_query": "select * from (\n \n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n\n \n \n \n \n\n \n \n\n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n \n\n \n\n \n \n \n\n\n \n \n \n\n \n \n \n\n \n \n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_E6EC3C50E5_ELEMENTARY_SOURCE_VOLUME_ANOMALIES_TRAINING_ANY_TYPE_COLUMN_ANOMALIES_TRAINING___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n\n \n\n\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.ANY_TYPE_COLUMN_ANOMALIES_TRAINING' as varchar)) and\n metric_name = cast('row_count' as varchar)"}, "configuration": {"test_name": "volume_anomalies", "timestamp_column": null, "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:20+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "pass"}], "description": "There were no failures, no errors and no warnings on the last 1 test runs."}}], "source.elementary_integration_tests.training.string_column_anomalies_training": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a.None.event_freshness", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "string_column_anomalies_training", "column_name": null, "test_name": "event_freshness_anomalies", "test_display_name": "Event Freshness Anomalies", "latest_run_time": "2023-05-29T18:01:20+03:00", "latest_run_time_utc": "2023-05-29T15:01:20+00:00", "latest_run_status": "fail", "model_unique_id": "source.elementary_integration_tests.training.string_column_anomalies_training", "table_unique_id": "elementary_tests.test_seeds.string_column_anomalies_training", "test_type": "anomaly_detection", "test_sub_type": "event_freshness", "test_query": "select * from (\n \n\n \n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n\n \n \n \n \n\n \n \n\n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n \n\n \n\n \n \n \n\n\n \n \n \n\n \n \n \n\n \n \n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_4B2FB7183A_ELEMENTARY_SOURCE_EVENT_FRESHNESS_ANOMALIES_TRAINING_STRING_COLUMN_ANOMALIES_TRAINING_OCCURRED_AT__UPDATED_AT__ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n\n \n\n\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING' as varchar)) and\n metric_name = cast('event_freshness' as varchar)", "test_params": {"event_timestamp_column": "occurred_at", "update_timestamp_column": "updated_at", "model": "{{ get_where_subquery(source('training', 'string_column_anomalies_training')) }}", "sensitivity": 3, "timestamp_column": "updated_at", "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "The last event_freshness value is 86400.000. The average for this metric is 6360.000.", "result_query": "select * from (\n \n\n \n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n\n \n \n \n \n\n \n \n\n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n \n\n \n\n \n \n \n\n\n \n \n \n\n \n \n \n\n \n \n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_4B2FB7183A_ELEMENTARY_SOURCE_EVENT_FRESHNESS_ANOMALIES_TRAINING_STRING_COLUMN_ANOMALIES_TRAINING_OCCURRED_AT__UPDATED_AT__ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n\n \n\n\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING' as varchar)) and\n metric_name = cast('event_freshness' as varchar)"}, "configuration": {"test_name": "event_freshness_anomalies", "timestamp_column": "updated_at", "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 1.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:20+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af.None.freshness", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "string_column_anomalies_training", "column_name": null, "test_name": "freshness_anomalies", "test_display_name": "Freshness Anomalies", "latest_run_time": "2023-05-29T18:01:20+03:00", "latest_run_time_utc": "2023-05-29T15:01:20+00:00", "latest_run_status": "fail", "model_unique_id": "source.elementary_integration_tests.training.string_column_anomalies_training", "table_unique_id": "elementary_tests.test_seeds.string_column_anomalies_training", "test_type": "anomaly_detection", "test_sub_type": "freshness", "test_query": "select * from (\n \n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n\n \n \n \n \n\n \n \n\n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n \n\n \n\n \n \n \n\n\n \n \n \n\n \n \n \n\n \n \n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_4640CC98AF_ELEMENTARY_SOURCE_FRESHNESS_ANOMALIES_TRAINING_STRING_COLUMN_ANOMALIES_TRAINING___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n\n \n\n\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING' as varchar)) and\n metric_name = cast('freshness' as varchar)", "test_params": {"model": "{{ get_where_subquery(source('training', 'string_column_anomalies_training')) }}", "sensitivity": 3, "timestamp_column": "updated_at", "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "Last update was at 1969-12-30 00:00:00.000, 48.00 hours ago. Usually the table is updated within 24.80 hours.", "result_query": "select * from (\n \n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n\n \n \n \n \n\n \n \n\n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n \n\n \n\n \n \n \n\n\n \n \n \n\n \n \n \n\n \n \n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_4640CC98AF_ELEMENTARY_SOURCE_FRESHNESS_ANOMALIES_TRAINING_STRING_COLUMN_ANOMALIES_TRAINING___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n\n \n\n\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING' as varchar)) and\n metric_name = cast('freshness' as varchar)"}, "configuration": {"test_name": "freshness_anomalies", "timestamp_column": "updated_at", "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 1.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:20+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5", "elementary_unique_id": "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5.None.row_count", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "string_column_anomalies_training", "column_name": null, "test_name": "volume_anomalies", "test_display_name": "Volume Anomalies", "latest_run_time": "2023-05-29T18:01:20+03:00", "latest_run_time_utc": "2023-05-29T15:01:20+00:00", "latest_run_status": "fail", "model_unique_id": "source.elementary_integration_tests.training.string_column_anomalies_training", "table_unique_id": "elementary_tests.test_seeds.string_column_anomalies_training", "test_type": "anomaly_detection", "test_sub_type": "row_count", "test_query": "select * from (\n \n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n\n \n \n \n \n\n \n \n\n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n \n\n \n\n \n \n \n\n\n \n \n \n\n \n \n \n\n \n \n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_F934F558B5_ELEMENTARY_SOURCE_VOLUME_ANOMALIES_TRAINING_STRING_COLUMN_ANOMALIES_TRAINING___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n\n \n\n\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING' as varchar)) and\n metric_name = cast('row_count' as varchar)", "test_params": {"model": "{{ get_where_subquery(source('training', 'string_column_anomalies_training')) }}", "sensitivity": 3, "timestamp_column": "updated_at", "backfill_days": 2}, "test_created_at": null, "description": null, "result": {"result_description": "The last row_count value is 0.000. The average for this metric is 96.667.", "result_query": "select * from (\n \n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.monitors_runs\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.data_monitoring_metrics\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.alerts_anomaly_detection\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.metrics_anomaly_score\n -- depends_on: ELEMENTARY_TESTS.noya_tests_elementary.dbt_run_results\n\n \n \n \n \n\n \n \n\n \n\n \n \n \n \n \n \n\n \n \n \n\n \n \n \n\n\n \n \n \n \n\n \n \n \n \n \n\n \n\n \n \n \n\n\n \n \n \n\n \n \n \n\n \n \n \n \n\n\n with anomaly_scores as (\n select\n id,\n metric_id,\n test_execution_id,\n test_unique_id,\n detected_at,\n full_table_name,\n column_name,\n metric_name,\n anomaly_score,\n anomaly_score_threshold,\n anomalous_value,\n bucket_start,\n bucket_end,\n bucket_seasonality,\n metric_value,\n min_metric_value,\n max_metric_value,\n training_avg,\n training_stddev,\n training_set_size,\n training_start,\n training_end,\n dimension,\n dimension_value,\n \n case\n when dimension is not null then \n 'The last ' || metric_name || ' value for dimension ' || dimension || ' - ' ||\n case when dimension_value is null then 'NULL' else dimension_value end || ' is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when metric_name = 'freshness' then \n 'Last update was at ' || anomalous_value || ', ' || abs(round(cast(metric_value/3600 as numeric(28,6)), 2)) || ' hours ago. Usually the table is updated within ' || abs(round(cast(training_avg/3600 as numeric(28,6)), 2)) || ' hours.'\n\n when column_name is null then \n 'The last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n when column_name is not null then \n 'In column ' || column_name || ', the last ' || metric_name || ' value is ' || round(cast(metric_value as numeric(28,6)), 3) ||\n '. The average for this metric is ' || round(cast(training_avg as numeric(28,6)), 3) || '.'\n\n else null\n end as anomaly_description\n,\n max(bucket_end) over (partition by test_execution_id) as max_bucket_end\n from \"ELEMENTARY_TESTS\".\"NOYA_TESTS_ELEMENTARY\".\"TEST_F934F558B5_ELEMENTARY_SOURCE_VOLUME_ANOMALIES_TRAINING_STRING_COLUMN_ANOMALIES_TRAINING___ANOMALY_SCORES\"\n ),\n anomaly_scores_with_is_anomalous as (\n select\n *,\n case when\n anomaly_score is not null and\n case when metric_name IN \n ( 'freshness' , 'event_freshness' )\n then\n anomaly_score > 3\n else\n \n abs(anomaly_score) > 3\n \n\n end and\n bucket_end >= \n dateadd(day, cast('-2' as INT), cast(max_bucket_end as TIMESTAMP))\n and\n training_set_size >= 14\n then TRUE else FALSE end as is_anomalous\n from anomaly_scores\n )\n\n select\n metric_value as value,\n training_avg as average,\n \n case\n when is_anomalous = TRUE and 'both' = 'spike' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'spike' then\n lag(min_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'spike' then metric_value\n else min_metric_value end as min_value,\n case\n when is_anomalous = TRUE and 'both' = 'drop' then\n lag(metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when is_anomalous = TRUE and 'both' != 'drop' then\n lag(max_metric_value) over (partition by full_table_name, column_name, metric_name, dimension, dimension_value order by bucket_end)\n when 'both' = 'drop' then metric_value\n else max_metric_value end as max_value,\n bucket_start as start_time,\n bucket_end as end_time,\n *\n from anomaly_scores_with_is_anomalous\n order by bucket_end, dimension_value\n\n \n\n\n) results\n where\n anomaly_score is not null and\n upper(full_table_name) = upper(cast('ELEMENTARY_TESTS.TEST_SEEDS.STRING_COLUMN_ANOMALIES_TRAINING' as varchar)) and\n metric_name = cast('row_count' as varchar)"}, "configuration": {"test_name": "volume_anomalies", "timestamp_column": "updated_at", "testing_timeframe": "1 day", "anomaly_threshold": 3}}, "test_runs": {"fail_rate": 1.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:20+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}], "model.elementary_integration_tests.users_per_day_weekly_seasonal": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_users_per_day_weekly_seasonal_14__2__updated_at.6a5e06fe3b", "elementary_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_users_per_day_weekly_seasonal_14__2__updated_at.6a5e06fe3b.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "users_per_day_weekly_seasonal", "column_name": null, "test_name": "volume_anomalies", "test_display_name": "Volume Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.users_per_day_weekly_seasonal", "table_unique_id": "elementary_tests.noya_tests.users_per_day_weekly_seasonal", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"timestamp_column": "updated_at", "sensitivity": 2, "backfill_days": 14, "model": "{{ get_where_subquery(ref('users_per_day_weekly_seasonal')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "volume_anomalies", "timestamp_column": "updated_at", "testing_timeframe": "1 day", "anomaly_threshold": 2}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_users_per_day_weekly_seasonal_14__day_of_week__2__updated_at.306327230f", "elementary_unique_id": "test.elementary_integration_tests.elementary_volume_anomalies_users_per_day_weekly_seasonal_14__day_of_week__2__updated_at.306327230f.None.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "users_per_day_weekly_seasonal", "column_name": null, "test_name": "volume_anomalies", "test_display_name": "Volume Anomalies", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.users_per_day_weekly_seasonal", "table_unique_id": "elementary_tests.noya_tests.users_per_day_weekly_seasonal", "test_type": "anomaly_detection", "test_sub_type": "generic", "test_query": null, "test_params": {"timestamp_column": "updated_at", "sensitivity": 2, "backfill_days": 14, "seasonality": "day_of_week", "model": "{{ get_where_subquery(ref('users_per_day_weekly_seasonal')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "volume_anomalies", "timestamp_column": "updated_at", "testing_timeframe": "1 day", "anomaly_threshold": 2}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}], "null": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.singular_test_with_no_ref", "elementary_unique_id": "test.elementary_integration_tests.singular_test_with_no_ref.None.singular", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": null, "column_name": null, "test_name": "singular_test_with_no_ref", "test_display_name": "Singular Test With No Ref", "latest_run_time": "2023-05-29T18:01:12+03:00", "latest_run_time_utc": "2023-05-29T15:01:12+00:00", "latest_run_status": "fail", "model_unique_id": null, "table_unique_id": "elementary_tests.noya_tests", "test_type": "dbt_test", "test_sub_type": "singular", "test_query": "select min from ELEMENTARY_TESTS.noya_tests.numeric_column_anomalies where min < 100", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "Got 96 results, configured to fail if != 0", "result_query": "select min from ELEMENTARY_TESTS.noya_tests.numeric_column_anomalies where min < 100"}, "configuration": {"test_name": "singular_test_with_no_ref", "test_params": {}}}, "test_runs": {"fail_rate": 1.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": 96, "time_utc": "2023-05-29T15:01:12+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}, {"metadata": {"test_unique_id": "test.elementary_integration_tests.singular_test_with_two_refs", "elementary_unique_id": "test.elementary_integration_tests.singular_test_with_two_refs.None.singular", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": null, "column_name": null, "test_name": "singular_test_with_two_refs", "test_display_name": "Singular Test With Two Refs", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": null, "table_unique_id": "elementary_tests.noya_tests", "test_type": "dbt_test", "test_sub_type": "singular", "test_query": null, "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "singular_test_with_two_refs", "test_params": {}}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}], "source.elementary_integration_tests.training.numeric_column_anomalies_training": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.singular_test_with_source_ref", "elementary_unique_id": "test.elementary_integration_tests.singular_test_with_source_ref.None.singular", "database_name": "ELEMENTARY_TESTS", "schema_name": "test_seeds", "table_name": "numeric_column_anomalies_training", "column_name": null, "test_name": "singular_test_with_source_ref", "test_display_name": "Singular Test With Source Ref", "latest_run_time": "2023-05-29T18:01:12+03:00", "latest_run_time_utc": "2023-05-29T15:01:12+00:00", "latest_run_status": "fail", "model_unique_id": "source.elementary_integration_tests.training.numeric_column_anomalies_training", "table_unique_id": "elementary_tests.test_seeds.numeric_column_anomalies_training", "test_type": "dbt_test", "test_sub_type": "singular", "test_query": "select min from ELEMENTARY_TESTS.test_seeds.numeric_column_anomalies_training where min < 105", "test_params": {}, "test_created_at": null, "description": null, "result": {"result_description": "Got 302 results, configured to fail if != 0", "result_query": "select min from ELEMENTARY_TESTS.test_seeds.numeric_column_anomalies_training where min < 105"}, "configuration": {"test_name": "singular_test_with_source_ref", "test_params": {}}}, "test_runs": {"fail_rate": 1.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "invocations": [{"affected_rows": 302, "time_utc": "2023-05-29T15:01:12+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "fail"}], "description": "There were 1 failures, no errors and no warnings on the last 1 test runs."}}], "model.elementary_integration_tests.error_model": [{"metadata": {"test_unique_id": "test.elementary_integration_tests.uniques_error_model_missing_column.3700cec9df", "elementary_unique_id": "test.elementary_integration_tests.uniques_error_model_missing_column.3700cec9df.missing_column.generic", "database_name": "ELEMENTARY_TESTS", "schema_name": "noya_tests", "table_name": "error_model", "column_name": "missing_column", "test_name": "uniques", "test_display_name": "Uniques", "latest_run_time": "2023-05-29T18:01:32+03:00", "latest_run_time_utc": "2023-05-29T15:01:32+00:00", "latest_run_status": "skipped", "model_unique_id": "model.elementary_integration_tests.error_model", "table_unique_id": "elementary_tests.noya_tests.error_model", "test_type": "dbt_test", "test_sub_type": "generic", "test_query": null, "test_params": {"column_name": "missing_column", "model": "{{ get_where_subquery(ref('error_model')) }}"}, "test_created_at": null, "description": null, "result": {"result_description": null, "result_query": null}, "configuration": {"test_name": "uniques", "test_params": {"column_name": "missing_column", "model": "{{ get_where_subquery(ref('error_model')) }}"}}}, "test_runs": {"fail_rate": 0.0, "totals": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "invocations": [{"affected_rows": null, "time_utc": "2023-05-29T15:01:32+00:00", "id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "status": "skipped"}], "description": "There were no failures, no errors and no warnings on the last 0 test runs."}}]}, "test_runs_totals": {"model.elementary_integration_tests.one": {"errors": 0, "warnings": 0, "passed": 0, "failures": 7}, "model.elementary_integration_tests.config_levels_project": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "model.elementary_integration_tests.config_levels_test_and_model": {"errors": 0, "warnings": 0, "passed": 2, "failures": 0}, "model.elementary_integration_tests.any_type_column_anomalies": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "model.elementary_integration_tests.copy_numeric_column_anomalies": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "model.elementary_integration_tests.ephemeral_model": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "model.elementary_integration_tests.numeric_column_anomalies": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "model.elementary_integration_tests.string_column_anomalies": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "model.elementary_integration_tests.backfill_days_column_anomalies": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "model.elementary_integration_tests.no_timestamp_anomalies": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "model.elementary_integration_tests.dimension_anomalies": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "model.elementary_integration_tests.groups": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "model.elementary_integration_tests.stats_players": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "model.elementary_integration_tests.stats_team": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "source.elementary_integration_tests.validation.any_type_column_anomalies_validation": {"errors": 0, "warnings": 0, "passed": 58, "failures": 1}, "source.elementary_integration_tests.training.any_type_column_anomalies_training": {"errors": 1, "warnings": 0, "passed": 2, "failures": 0}, "source.elementary_integration_tests.training.string_column_anomalies_training": {"errors": 0, "warnings": 0, "passed": 0, "failures": 3}, "model.elementary_integration_tests.users_per_day_weekly_seasonal": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "null": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "source.elementary_integration_tests.training.numeric_column_anomalies_training": {"errors": 0, "warnings": 0, "passed": 0, "failures": 1}, "model.elementary_integration_tests.error_model": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}}, "coverages": {"source.elementary_integration_tests.training.numeric_column_anomalies_training": {"table_tests": 1, "column_tests": 0}, "model.elementary_integration_tests.numeric_column_anomalies": {"table_tests": 8, "column_tests": 17}, "model.elementary_integration_tests.one": {"table_tests": 0, "column_tests": 1}, "model.elementary_integration_tests.no_timestamp_anomalies": {"table_tests": 2, "column_tests": 1}, "model.elementary_integration_tests.dimension_anomalies": {"table_tests": 5, "column_tests": 0}, "model.elementary_integration_tests.error_model": {"table_tests": 0, "column_tests": 1}, "model.elementary_integration_tests.string_column_anomalies": {"table_tests": 4, "column_tests": 7}, "model.elementary_integration_tests.copy_numeric_column_anomalies": {"table_tests": 1, "column_tests": 0}, "model.elementary_integration_tests.stats_players": {"table_tests": 3, "column_tests": 0}, "model.elementary_integration_tests.users_per_day_weekly_seasonal": {"table_tests": 2, "column_tests": 0}, "model.elementary_integration_tests.ephemeral_model": {"table_tests": 4, "column_tests": 0}, "model.elementary_integration_tests.config_levels_test_and_model": {"table_tests": 2, "column_tests": 0}, "source.elementary_integration_tests.training.any_type_column_anomalies_training": {"table_tests": 3, "column_tests": 0}, "source.elementary_integration_tests.training.string_column_anomalies_training": {"table_tests": 3, "column_tests": 0}, "model.elementary_integration_tests.backfill_days_column_anomalies": {"table_tests": 0, "column_tests": 2}, "source.elementary_integration_tests.validation.any_type_column_anomalies_validation": {"table_tests": 1, "column_tests": 1}, "model.elementary_integration_tests.any_type_column_anomalies": {"table_tests": 6, "column_tests": 0}, "model.elementary_integration_tests.groups": {"table_tests": 3, "column_tests": 0}, "model.elementary_integration_tests.stats_team": {"table_tests": 1, "column_tests": 0}, "model.elementary_integration_tests.config_levels_project": {"table_tests": 1, "column_tests": 0}}, "model_runs": [{"unique_id": "model.elementary_integration_tests.one", "schema": "noya_tests", "name": "one", "status": "success", "last_exec_time": 1.7, "median_exec_time": 1.7, "exec_time_change_rate": 0.0, "totals": {"errors": 0, "success": 1}, "runs": [{"id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "time_utc": "2023-05-29T15:01:30+00:00", "status": "success", "full_refresh": false, "materialization": "view", "execution_time": 1.7}]}, {"unique_id": "model.elementary_integration_tests.test_alerts_union", "schema": "noya_tests", "name": "test_alerts_union", "status": "success", "last_exec_time": 0.8, "median_exec_time": 0.8, "exec_time_change_rate": 0.0, "totals": {"errors": 0, "success": 1}, "runs": [{"id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "time_utc": "2023-05-29T15:01:30+00:00", "status": "success", "full_refresh": false, "materialization": "view", "execution_time": 0.8}]}, {"unique_id": "model.elementary_integration_tests.copy_numeric_column_anomalies", "schema": "noya_tests", "name": "copy_numeric_column_anomalies", "status": "skipped", "last_exec_time": 0.0, "median_exec_time": 0.0, "exec_time_change_rate": 0.0, "totals": {"errors": 0, "success": 0}, "runs": [{"id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "time_utc": "2023-05-29T15:01:30+00:00", "status": "skipped", "full_refresh": false, "materialization": "view", "execution_time": 0.0}]}, {"unique_id": "model.elementary_integration_tests.string_column_anomalies", "schema": "noya_tests", "name": "string_column_anomalies", "status": "skipped", "last_exec_time": 0.0, "median_exec_time": 0.0, "exec_time_change_rate": 0.0, "totals": {"errors": 0, "success": 0}, "runs": [{"id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "time_utc": "2023-05-29T15:01:30+00:00", "status": "skipped", "full_refresh": false, "materialization": "view", "execution_time": 0.0}]}, {"unique_id": "model.elementary_integration_tests.numeric_column_anomalies", "schema": "noya_tests", "name": "numeric_column_anomalies", "status": "skipped", "last_exec_time": 0.0, "median_exec_time": 0.0, "exec_time_change_rate": 0.0, "totals": {"errors": 0, "success": 0}, "runs": [{"id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "time_utc": "2023-05-29T15:01:30+00:00", "status": "skipped", "full_refresh": false, "materialization": "view", "execution_time": 0.0}]}, {"unique_id": "model.elementary_integration_tests.no_timestamp_anomalies", "schema": "noya_tests", "name": "no_timestamp_anomalies", "status": "skipped", "last_exec_time": 0.0, "median_exec_time": 0.0, "exec_time_change_rate": 0.0, "totals": {"errors": 0, "success": 0}, "runs": [{"id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "time_utc": "2023-05-29T15:01:30+00:00", "status": "skipped", "full_refresh": false, "materialization": "view", "execution_time": 0.0}]}, {"unique_id": "model.elementary_integration_tests.dimension_anomalies", "schema": "noya_tests", "name": "dimension_anomalies", "status": "skipped", "last_exec_time": 0.0, "median_exec_time": 0.0, "exec_time_change_rate": 0.0, "totals": {"errors": 0, "success": 0}, "runs": [{"id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "time_utc": "2023-05-29T15:01:30+00:00", "status": "skipped", "full_refresh": false, "materialization": "view", "execution_time": 0.0}]}, {"unique_id": "model.elementary_integration_tests.any_type_column_anomalies", "schema": "noya_tests", "name": "any_type_column_anomalies", "status": "skipped", "last_exec_time": 0.0, "median_exec_time": 0.0, "exec_time_change_rate": 0.0, "totals": {"errors": 0, "success": 0}, "runs": [{"id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "time_utc": "2023-05-29T15:01:30+00:00", "status": "skipped", "full_refresh": false, "materialization": "view", "execution_time": 0.0}]}, {"unique_id": "model.elementary_integration_tests.backfill_days_column_anomalies", "schema": "noya_tests", "name": "backfill_days_column_anomalies", "status": "skipped", "last_exec_time": 0.0, "median_exec_time": 0.0, "exec_time_change_rate": 0.0, "totals": {"errors": 0, "success": 0}, "runs": [{"id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "time_utc": "2023-05-29T15:01:30+00:00", "status": "skipped", "full_refresh": false, "materialization": "view", "execution_time": 0.0}]}, {"unique_id": "model.elementary_integration_tests.users_per_day_weekly_seasonal", "schema": "noya_tests", "name": "users_per_day_weekly_seasonal", "status": "error", "last_exec_time": 0.0, "median_exec_time": 0.0, "exec_time_change_rate": 0.0, "totals": {"errors": 1, "success": 0}, "runs": [{"id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "time_utc": "2023-05-29T15:01:30+00:00", "status": "error", "full_refresh": false, "materialization": "view", "execution_time": 0.0}]}, {"unique_id": "model.elementary_integration_tests.nested", "schema": "noya_tests", "name": "nested", "status": "success", "last_exec_time": 1.7, "median_exec_time": 1.7, "exec_time_change_rate": 0.0, "totals": {"errors": 0, "success": 1}, "runs": [{"id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "time_utc": "2023-05-29T15:01:30+00:00", "status": "success", "full_refresh": false, "materialization": "view", "execution_time": 1.7}]}, {"unique_id": "model.elementary_integration_tests.config_levels_test_and_model", "schema": "noya_tests", "name": "config_levels_test_and_model", "status": "success", "last_exec_time": 1.7, "median_exec_time": 1.7, "exec_time_change_rate": 0.0, "totals": {"errors": 0, "success": 1}, "runs": [{"id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "time_utc": "2023-05-29T15:01:30+00:00", "status": "success", "full_refresh": false, "materialization": "view", "execution_time": 1.7}]}, {"unique_id": "model.elementary_integration_tests.config_levels_project", "schema": "noya_tests", "name": "config_levels_project", "status": "success", "last_exec_time": 1.7, "median_exec_time": 1.7, "exec_time_change_rate": 0.0, "totals": {"errors": 0, "success": 1}, "runs": [{"id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "time_utc": "2023-05-29T15:01:30+00:00", "status": "success", "full_refresh": false, "materialization": "view", "execution_time": 1.7}]}, {"unique_id": "model.elementary_integration_tests.error_model", "schema": "noya_tests", "name": "error_model", "status": "error", "last_exec_time": 0.0, "median_exec_time": 0.0, "exec_time_change_rate": 0.0, "totals": {"errors": 1, "success": 0}, "runs": [{"id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "time_utc": "2023-05-29T15:01:30+00:00", "status": "error", "full_refresh": false, "materialization": "view", "execution_time": 0.0}]}, {"unique_id": "model.elementary_integration_tests.stats_team", "schema": "noya_tests", "name": "stats_team", "status": "error", "last_exec_time": 0.0, "median_exec_time": 0.0, "exec_time_change_rate": 0.0, "totals": {"errors": 1, "success": 0}, "runs": [{"id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "time_utc": "2023-05-29T15:01:30+00:00", "status": "error", "full_refresh": false, "materialization": "view", "execution_time": 0.0}]}, {"unique_id": "model.elementary_integration_tests.stats_players", "schema": "noya_tests", "name": "stats_players", "status": "error", "last_exec_time": 0.0, "median_exec_time": 0.0, "exec_time_change_rate": 0.0, "totals": {"errors": 1, "success": 0}, "runs": [{"id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "time_utc": "2023-05-29T15:01:30+00:00", "status": "error", "full_refresh": false, "materialization": "view", "execution_time": 0.0}]}, {"unique_id": "model.elementary_integration_tests.groups", "schema": "noya_tests", "name": "groups", "status": "error", "last_exec_time": 0.0, "median_exec_time": 0.0, "exec_time_change_rate": 0.0, "totals": {"errors": 1, "success": 0}, "runs": [{"id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "time_utc": "2023-05-29T15:01:30+00:00", "status": "error", "full_refresh": false, "materialization": "view", "execution_time": 0.0}]}], "model_runs_totals": {"model.elementary_integration_tests.one": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "model.elementary_integration_tests.test_alerts_union": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "model.elementary_integration_tests.copy_numeric_column_anomalies": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "model.elementary_integration_tests.string_column_anomalies": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "model.elementary_integration_tests.numeric_column_anomalies": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "model.elementary_integration_tests.no_timestamp_anomalies": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "model.elementary_integration_tests.dimension_anomalies": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "model.elementary_integration_tests.any_type_column_anomalies": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "model.elementary_integration_tests.backfill_days_column_anomalies": {"errors": 0, "warnings": 0, "passed": 0, "failures": 0}, "model.elementary_integration_tests.users_per_day_weekly_seasonal": {"errors": 1, "warnings": 0, "passed": 0, "failures": 0}, "model.elementary_integration_tests.nested": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "model.elementary_integration_tests.config_levels_test_and_model": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "model.elementary_integration_tests.config_levels_project": {"errors": 0, "warnings": 0, "passed": 1, "failures": 0}, "model.elementary_integration_tests.error_model": {"errors": 1, "warnings": 0, "passed": 0, "failures": 0}, "model.elementary_integration_tests.stats_team": {"errors": 1, "warnings": 0, "passed": 0, "failures": 0}, "model.elementary_integration_tests.stats_players": {"errors": 1, "warnings": 0, "passed": 0, "failures": 0}, "model.elementary_integration_tests.groups": {"errors": 1, "warnings": 0, "passed": 0, "failures": 0}}, "filters": {"test_results": [{"name": "failures", "display_name": "Failures", "model_unique_ids": ["source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "source.elementary_integration_tests.training.numeric_column_anomalies_training", "model.elementary_integration_tests.one", null, "source.elementary_integration_tests.training.string_column_anomalies_training"]}, {"name": "errors", "display_name": "Errors", "model_unique_ids": ["source.elementary_integration_tests.training.any_type_column_anomalies_training"]}, {"name": "passed", "display_name": "Passed", "model_unique_ids": ["model.elementary_integration_tests.config_levels_test_and_model", "model.elementary_integration_tests.config_levels_project", "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "source.elementary_integration_tests.training.any_type_column_anomalies_training"]}, {"name": "no_test", "display_name": "No Tests", "model_unique_ids": ["model.elementary_integration_tests.copy_numeric_column_anomalies", "model.elementary_integration_tests.numeric_column_anomalies", "model.elementary_integration_tests.no_timestamp_anomalies", "model.elementary_integration_tests.stats_players", "model.elementary_integration_tests.any_type_column_anomalies", "model.elementary_integration_tests.users_per_day_weekly_seasonal", "source.elementary_integration_tests.training.users_per_day_weekly_seasonal_training", "model.elementary_integration_tests.dimension_anomalies", "source.elementary_integration_tests.validation.users_per_day_weekly_seasonal_validation", "model.elementary_integration_tests.stats_team", "model.elementary_integration_tests.groups", "model.elementary_integration_tests.error_model", "model.elementary_integration_tests.string_column_anomalies", "model.elementary_integration_tests.backfill_days_column_anomalies", "model.elementary_integration_tests.test_alerts_union", "model.elementary_integration_tests.ephemeral_model", "model.elementary_integration_tests.nested"]}], "test_runs": [{"name": "failures", "display_name": "Failures", "model_unique_ids": ["source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "source.elementary_integration_tests.training.numeric_column_anomalies_training", "model.elementary_integration_tests.one", null, "source.elementary_integration_tests.training.string_column_anomalies_training"]}, {"name": "errors", "display_name": "Errors", "model_unique_ids": ["source.elementary_integration_tests.training.any_type_column_anomalies_training"]}, {"name": "passed", "display_name": "Passed", "model_unique_ids": ["model.elementary_integration_tests.config_levels_test_and_model", "model.elementary_integration_tests.config_levels_project", "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "source.elementary_integration_tests.training.any_type_column_anomalies_training"]}, {"name": "no_test", "display_name": "No Tests", "model_unique_ids": ["model.elementary_integration_tests.copy_numeric_column_anomalies", "model.elementary_integration_tests.numeric_column_anomalies", "model.elementary_integration_tests.no_timestamp_anomalies", "model.elementary_integration_tests.stats_players", "model.elementary_integration_tests.any_type_column_anomalies", "model.elementary_integration_tests.users_per_day_weekly_seasonal", "source.elementary_integration_tests.training.users_per_day_weekly_seasonal_training", "model.elementary_integration_tests.dimension_anomalies", "source.elementary_integration_tests.validation.users_per_day_weekly_seasonal_validation", "model.elementary_integration_tests.stats_team", "model.elementary_integration_tests.groups", "model.elementary_integration_tests.error_model", "model.elementary_integration_tests.string_column_anomalies", "model.elementary_integration_tests.backfill_days_column_anomalies", "model.elementary_integration_tests.test_alerts_union", "model.elementary_integration_tests.ephemeral_model", "model.elementary_integration_tests.nested"]}], "model_runs": [{"name": "success", "display_name": "Successful Runs", "model_unique_ids": ["model.elementary_integration_tests.one", "model.elementary_integration_tests.nested", "model.elementary_integration_tests.config_levels_test_and_model", "model.elementary_integration_tests.config_levels_project", "model.elementary_integration_tests.test_alerts_union"]}, {"name": "errors", "display_name": "Failed Runs", "model_unique_ids": ["model.elementary_integration_tests.stats_players", "model.elementary_integration_tests.users_per_day_weekly_seasonal", "model.elementary_integration_tests.stats_team", "model.elementary_integration_tests.groups", "model.elementary_integration_tests.error_model"]}, {"name": "no_runs", "display_name": "No Runs", "model_unique_ids": ["model.elementary_integration_tests.copy_numeric_column_anomalies", "model.elementary_integration_tests.numeric_column_anomalies", "model.elementary_integration_tests.no_timestamp_anomalies", "model.elementary_integration_tests.backfill_days_column_anomalies", "model.elementary_integration_tests.dimension_anomalies", "model.elementary_integration_tests.string_column_anomalies", "model.elementary_integration_tests.any_type_column_anomalies"]}]}, "lineage": {"nodes": [{"id": "model.elementary_integration_tests.stats_team", "type": "model"}, {"id": "model.elementary_integration_tests.copy_numeric_column_anomalies", "type": "model"}, {"id": "model.elementary_integration_tests.backfill_days_column_anomalies", "type": "model"}, {"id": "model.elementary_integration_tests.config_levels_project", "type": "model"}, {"id": "model.elementary_integration_tests.one", "type": "model"}, {"id": "model.elementary_integration_tests.config_levels_test_and_model", "type": "model"}, {"id": "model.elementary_integration_tests.users_per_day_weekly_seasonal", "type": "model"}, {"id": "model.elementary_integration_tests.dimension_anomalies", "type": "model"}, {"id": "model.elementary_integration_tests.stats_players", "type": "model"}, {"id": "model.elementary_integration_tests.groups", "type": "model"}, {"id": "model.elementary_integration_tests.no_timestamp_anomalies", "type": "model"}, {"id": "model.elementary_integration_tests.ephemeral_model", "type": "model"}, {"id": "model.elementary_integration_tests.numeric_column_anomalies", "type": "model"}, {"id": "model.elementary_integration_tests.string_column_anomalies", "type": "model"}, {"id": "model.elementary_integration_tests.test_alerts_union", "type": "model"}, {"id": "model.elementary_integration_tests.any_type_column_anomalies", "type": "model"}, {"id": "model.elementary_integration_tests.error_model", "type": "model"}, {"id": "model.elementary_integration_tests.nested", "type": "model"}, {"id": "source.elementary_integration_tests.training.any_type_column_anomalies_training", "type": "source"}, {"id": "source.elementary_integration_tests.training.string_column_anomalies_training", "type": "source"}, {"id": "source.elementary_integration_tests.training.numeric_column_anomalies_training", "type": "source"}, {"id": "source.elementary_integration_tests.training.users_per_day_weekly_seasonal_training", "type": "source"}, {"id": "source.elementary_integration_tests.validation.any_type_column_anomalies_validation", "type": "source"}, {"id": "source.elementary_integration_tests.validation.users_per_day_weekly_seasonal_validation", "type": "source"}, {"id": "exposure.elementary_integration_tests.elementary_exposure", "type": "exposure"}, {"id": "exposure.elementary_integration_tests.weekly_jaffle_metrics", "type": "exposure"}], "edges": [["model.elementary_integration_tests.copy_numeric_column_anomalies", "model.elementary_integration_tests.numeric_column_anomalies"], ["model.elementary_integration_tests.users_per_day_weekly_seasonal", "source.elementary_integration_tests.training.users_per_day_weekly_seasonal_training"], ["model.elementary_integration_tests.test_alerts_union", "model.elementary.alerts_anomaly_detection"], ["model.elementary_integration_tests.test_alerts_union", "model.elementary.alerts_dbt_tests"], ["model.elementary_integration_tests.test_alerts_union", "model.elementary.alerts_schema_changes"], ["exposure.elementary_integration_tests.elementary_exposure", "model.elementary_integration_tests.error_model"], ["exposure.elementary_integration_tests.elementary_exposure", "source.elementary_integration_tests.training.any_type_column_anomalies_training"], ["exposure.elementary_integration_tests.weekly_jaffle_metrics", "model.elementary_integration_tests.numeric_column_anomalies"], ["exposure.elementary_integration_tests.weekly_jaffle_metrics", "model.elementary_integration_tests.string_column_anomalies"]]}, "invocations": [{"invocation_id": "8e3c93a6-77b4-4de0-9778-475945f3a741", "detected_at": null, "command": "build", "selected": "", "full_refresh": false, "job_url": null, "job_name": "test multi", "job_id": null, "orchestrator": "airflow"}, {"invocation_id": "63973590-b12a-436c-8b2b-62f306e31de9", "detected_at": null, "command": "test", "selected": "one", "full_refresh": false, "job_url": null, "job_name": null, "job_id": null, "orchestrator": null}], "resources_latest_invocation": {"test.elementary_integration_tests.elementary_schema_changes_groups_.b321d3c7ba": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_any_type_column_anomalies_training_occurred_at.50c4b3e11b": "8e3c93a6-77b4-4de0-9778-475945f3a741", "model.elementary_integration_tests.dimension_anomalies": "8e3c93a6-77b4-4de0-9778-475945f3a741", "model.elementary.snapshot_run_results": "8e3c93a6-77b4-4de0-9778-475945f3a741", "snapshot.elementary_integration_tests.failed_snapshot": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_all_columns_anomalies_string_column_anomalies_.151e5a09d5": "8e3c93a6-77b4-4de0-9778-475945f3a741", "model.elementary.filtered_information_schema_tables": "8e3c93a6-77b4-4de0-9778-475945f3a741", "model.elementary.alerts_dbt_source_freshness": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_week__1.ef6d0eff06": "8e3c93a6-77b4-4de0-9778-475945f3a741", "model.elementary.metadata": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_freshness_anomalies_ephemeral_model_.085bb21469": "8e3c93a6-77b4-4de0-9778-475945f3a741", "model.elementary_integration_tests.nested": "8e3c93a6-77b4-4de0-9778-475945f3a741", "model.elementary_integration_tests.config_levels_test_and_model": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_volume_anomalies_users_per_day_weekly_seasonal_14__day_of_week__2__updated_at.306327230f": "8e3c93a6-77b4-4de0-9778-475945f3a741", "model.elementary.dbt_snapshots": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__max.9841e551cc": "8e3c93a6-77b4-4de0-9778-475945f3a741", "model.elementary_integration_tests.stats_players": "8e3c93a6-77b4-4de0-9778-475945f3a741", "model.elementary.dbt_sources": "8e3c93a6-77b4-4de0-9778-475945f3a741", "model.elementary.elementary_test_results": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_column_anomalies_no_timestamp_anomalies_null_count__null_count_str.6532606df5": "8e3c93a6-77b4-4de0-9778-475945f3a741", "model.elementary_integration_tests.error_model": "8e3c93a6-77b4-4de0-9778-475945f3a741", "seed.elementary_integration_tests.users_per_day_weekly_seasonal_training": "8e3c93a6-77b4-4de0-9778-475945f3a741", "model.elementary.dbt_columns": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_schema_changes_string_column_anomalies_.c57cd75b87": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_column_anomalies_backfill_days_column_anomalies_min_length__max_length__min_length.9cf2f5f6ad": "8e3c93a6-77b4-4de0-9778-475945f3a741", "model.elementary.job_run_results": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_sum__sum.6ede4629eb": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_schema_changes_numeric_column_anomalies_.1d9f82cc93": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__min.6758a0b107": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_schema_changes_stats_team_.fe2147cc27": "8e3c93a6-77b4-4de0-9778-475945f3a741", "model.elementary_integration_tests.numeric_column_anomalies": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_freshness_anomalies_string_column_anomalies_.a7ac271e93": "8e3c93a6-77b4-4de0-9778-475945f3a741", "model.elementary.alerts_dbt_models": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_string_column_anomalies_training_.4640cc98af": "8e3c93a6-77b4-4de0-9778-475945f3a741", "model.elementary.test_result_rows": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies_drop.3e202bb243": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_event_freshness_anomalies_string_column_anomalies_occurred_at__updated_at.e4db4306c6": "8e3c93a6-77b4-4de0-9778-475945f3a741", "seed.elementary_integration_tests.numeric_column_anomalies_training": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_min_length__max_length__missing_count__min_length.d53c1dd4f8": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_average_length__null_count__average_length.fff5e03db2": "8e3c93a6-77b4-4de0-9778-475945f3a741", "seed.elementary_integration_tests.any_type_column_anomalies_validation": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_percent.096feb21e8": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_spike__average__max.d74f23e2ef": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_dimension_anomalies_dimension_anomalies_platform__version.b65d46fbf9": "8e3c93a6-77b4-4de0-9778-475945f3a741", "model.elementary_integration_tests.string_column_anomalies": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_volume_anomalies_any_type_column_anomalies_hour__4.98087b43c5": "8e3c93a6-77b4-4de0-9778-475945f3a741", "model.elementary.dbt_metrics": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_schema_changes_from_baseline_stats_players_.02157887f9": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.uniques_error_model_missing_column.3700cec9df": "8e3c93a6-77b4-4de0-9778-475945f3a741", "snapshot.elementary_integration_tests.test_alerts_union_snapshot": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_.23744bdf38": "8e3c93a6-77b4-4de0-9778-475945f3a741", "seed.elementary_integration_tests.users_per_day_weekly_seasonal_validation": "8e3c93a6-77b4-4de0-9778-475945f3a741", "model.elementary.anomaly_threshold_sensitivity": "8e3c93a6-77b4-4de0-9778-475945f3a741", "model.elementary.model_run_results": "8e3c93a6-77b4-4de0-9778-475945f3a741", "model.elementary_integration_tests.any_type_column_anomalies": "8e3c93a6-77b4-4de0-9778-475945f3a741", "seed.elementary_integration_tests.groups_validation": "8e3c93a6-77b4-4de0-9778-475945f3a741", "model.elementary_integration_tests.users_per_day_weekly_seasonal": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__max.21a73d9fec": "8e3c93a6-77b4-4de0-9778-475945f3a741", "model.elementary.data_monitoring_metrics": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_drop.c2dea8af95": "8e3c93a6-77b4-4de0-9778-475945f3a741", "model.elementary.dbt_tests": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.singular_test_with_no_ref": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_dimension_anomalies_no_timestamp_anomalies_null_count_str.cf20940f2d": "8e3c93a6-77b4-4de0-9778-475945f3a741", "seed.elementary_integration_tests.groups_training": "8e3c93a6-77b4-4de0-9778-475945f3a741", "model.elementary.alerts_anomaly_detection": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_source_all_columns_anomalies_validation_any_type_column_anomalies_validation_.0a44bf5c67": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_source_volume_anomalies_training_string_column_anomalies_training_.f934f558b5": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.singular_test_with_one_ref": "8e3c93a6-77b4-4de0-9778-475945f3a741", "model.elementary.dbt_seeds": "8e3c93a6-77b4-4de0-9778-475945f3a741", "model.elementary_integration_tests.copy_numeric_column_anomalies": "8e3c93a6-77b4-4de0-9778-475945f3a741", "seed.elementary_integration_tests.any_type_column_anomalies_training": "8e3c93a6-77b4-4de0-9778-475945f3a741", "seed.elementary_integration_tests.stats_players_training": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.singular_test_with_two_refs": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_count.bc3862520d": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_variance.ccbeab9e37": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_drop__average__max.e87fc4578f": "8e3c93a6-77b4-4de0-9778-475945f3a741", "model.elementary.schema_columns_snapshot": "8e3c93a6-77b4-4de0-9778-475945f3a741", "model.elementary_integration_tests.backfill_days_column_anomalies": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_volume_anomalies_ephemeral_model_.4b08aa00b7": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__min.8109482bd7": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.accepted_values_one_one__2__3.5c148cffcc": "63973590-b12a-436c-8b2b-62f306e31de9", "model.elementary.dbt_artifacts_hashes": "8e3c93a6-77b4-4de0-9778-475945f3a741", "model.elementary.alerts_dbt_tests": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_standard_deviation.85070e7e84": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.config_levels_config_levels_project__period_day_count_1_.e491a0d999": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_dimension_anomalies_dimension_anomalies_platform__platform_android_.89f503656a": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_schema_changes_from_baseline_groups_True.7468e2e161": "8e3c93a6-77b4-4de0-9778-475945f3a741", "model.elementary_integration_tests.one": "8e3c93a6-77b4-4de0-9778-475945f3a741", "seed.elementary_integration_tests.stats_team_training": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.relationships_string_column_anomalies_min_length__max_length__source_training_string_column_anomalies_training_.a2cbc353c6": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_volume_anomalies_users_per_day_weekly_seasonal_14__2__updated_at.6a5e06fe3b": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_freshness_anomalies_numeric_column_anomalies_.2047aacf6c": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_column_anomalies_backfill_days_column_anomalies_7__min_length__max_length__min_length.437faf5372": "8e3c93a6-77b4-4de0-9778-475945f3a741", "seed.elementary_integration_tests.stats_players_validation": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_schema_changes_stats_players_.388c33d77b": "8e3c93a6-77b4-4de0-9778-475945f3a741", "seed.elementary_integration_tests.dimension_anomalies_validation": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_volume_anomalies_no_timestamp_anomalies_.73ede8a6cc": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_all_columns_anomalies_numeric_column_anomalies_average_length__null_count.4719a95b87": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_zero_count.35e5387f41": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_max__average.fe144797ba": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_max_length.5c7beb9c06": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_volume_anomalies_numeric_column_anomalies_spike.d99efa2f8a": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__min.72357fb8ab": "8e3c93a6-77b4-4de0-9778-475945f3a741", "seed.elementary_integration_tests.stats_team_validation": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.config_levels_config_levels_test_and_model__period_hour_count_4___hour__4.4745d4716f": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_schema_changes_ephemeral_model_.4470433d4c": "8e3c93a6-77b4-4de0-9778-475945f3a741", "seed.elementary_integration_tests.string_column_anomalies_training": "8e3c93a6-77b4-4de0-9778-475945f3a741", "seed.elementary_integration_tests.dimension_anomalies_training": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_updated_at.8901e974a6": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_dimension_anomalies_dimension_anomalies_platform.cf343e4b29": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_dimension_anomalies_dimension_anomalies_drop__platform.021a36a88a": "8e3c93a6-77b4-4de0-9778-475945f3a741", "seed.elementary_integration_tests.string_column_anomalies_validation": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.source_generic_test_on_column_validation_any_type_column_anomalies_validation_null_count_int.13c361724d": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.singular_test_with_source_ref": "8e3c93a6-77b4-4de0-9778-475945f3a741", "model.elementary.alerts_schema_changes": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_all_columns_anomalies_ephemeral_model_.69d9c5e486": "8e3c93a6-77b4-4de0-9778-475945f3a741", "seed.elementary_integration_tests.numeric_column_anomalies_validation": "8e3c93a6-77b4-4de0-9778-475945f3a741", "model.elementary.metrics_anomaly_score": "8e3c93a6-77b4-4de0-9778-475945f3a741", "model.elementary_integration_tests.stats_team": "8e3c93a6-77b4-4de0-9778-475945f3a741", "model.elementary_integration_tests.test_alerts_union": "8e3c93a6-77b4-4de0-9778-475945f3a741", "model.elementary.dbt_exposures": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_source_event_freshness_anomalies_training_string_column_anomalies_training_occurred_at__updated_at.4b2fb7183a": "8e3c93a6-77b4-4de0-9778-475945f3a741", "seed.elementary_integration_tests.backfill_days_column_anomalies_training": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_source_volume_anomalies_training_any_type_column_anomalies_training_.e6ec3c50e5": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies_spike.1f1909a57c": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_schema_changes_from_baseline_groups_True.973df95f83": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_source_freshness_anomalies_training_any_type_column_anomalies_training_.5733415eda": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_all_columns_anomalies_any_type_column_anomalies__column1_column2_column3_column4_column5_column6_column7_column8_column9_column10_column11_column12_column13_column14_column15_column16_column17_.7ea0ad356c": "8e3c93a6-77b4-4de0-9778-475945f3a741", "model.elementary_integration_tests.config_levels_project": "8e3c93a6-77b4-4de0-9778-475945f3a741", "model.elementary.dbt_source_freshness_results": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_column_anomalies_string_column_anomalies_missing_percent.54024c1771": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_all_columns_anomalies_copy_numeric_column_anomalies_zero_count.9963113148": "8e3c93a6-77b4-4de0-9778-475945f3a741", "model.elementary.monitors_runs": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_min__average.e51df9913e": "8e3c93a6-77b4-4de0-9778-475945f3a741", "model.elementary_integration_tests.groups": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.generic_test_on_model_any_type_column_anomalies_.a9e77d8087": "8e3c93a6-77b4-4de0-9778-475945f3a741", "model.elementary.dbt_models": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__max.ea408823cb": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.config_levels_config_levels_test_and_model__period_day_count_3_.462f85ebd5": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_updated_at.b0d22411ff": "8e3c93a6-77b4-4de0-9778-475945f3a741", "seed.elementary_integration_tests.backfill_days_column_anomalies_validation": "8e3c93a6-77b4-4de0-9778-475945f3a741", "model.elementary.dbt_invocations": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_dimension_anomalies_dimension_anomalies_spike__platform.ae9aad0d02": "8e3c93a6-77b4-4de0-9778-475945f3a741", "model.elementary_integration_tests.no_timestamp_anomalies": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_schema_changes_from_baseline_stats_players_True.1447622942": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_column_anomalies_numeric_column_anomalies_average__average.a154426bde": "8e3c93a6-77b4-4de0-9778-475945f3a741", "model.elementary.filtered_information_schema_columns": "8e3c93a6-77b4-4de0-9778-475945f3a741", "model.elementary.dbt_run_results": "8e3c93a6-77b4-4de0-9778-475945f3a741", "test.elementary_integration_tests.elementary_event_freshness_anomalies_numeric_column_anomalies_occurred_at__updated_at.1ca7b9299b": "8e3c93a6-77b4-4de0-9778-475945f3a741"}, "env": {"project_name": "elementary_integration_tests", "env": "dev"}, "tracking": {"posthog_api_key": "phc_56XBEzZmh02mGkadqLiYW51eECyYKWPyecVwkGdGUfg", "report_generator_anonymous_user_id": "338938a8-199f-4127-8016-3a01132c0021", "anonymous_warehouse_id": "d85dcf226bee445349fe2f1d0bd60c5f54f9177170087d2610569dd0f297c1a2"}} \ No newline at end of file From 5f127cb215ce0316ce55432da01152d2cfb85196 Mon Sep 17 00:00:00 2001 From: Noy Arie Date: Wed, 31 May 2023 16:29:44 +0300 Subject: [PATCH 16/16] replace index.html --- .../monitor/data_monitoring/report/index.html | 940 ++++++++++-------- 1 file changed, 538 insertions(+), 402 deletions(-) diff --git a/elementary/monitor/data_monitoring/report/index.html b/elementary/monitor/data_monitoring/report/index.html index 01b96c3b2..04a9dacbb 100644 --- a/elementary/monitor/data_monitoring/report/index.html +++ b/elementary/monitor/data_monitoring/report/index.html @@ -19,7 +19,7 @@